The vehicle's heating, ventilation, and air-conditioning systems (or Mobile Air-Conditioning systems, MAC), are the most power-consuming auxiliaries, affecting the vehicle's power consumption in all powertrains. MAC power consumption reduces the electric range in electrified vehicles and increases the CO2 emissions in internal combustion engine vehicles. The MAC consumption assessment is crucial for the decarbonisation of the transport sector. Prediction of MAC energy using simulation models optimizes this assessment, reducing the testing burden, and allowing further analysis of varying exterior conditions and component efficiencies. This paper proposes a thermal model for a vehicle MAC system to estimate the cabin temperature and MAC energy consumption during a WLTC. The A/C compressor power demand is simulated through the refrigerant's semi-Ideal Vapour-Compression Cycle. The model considered a Proportional-Integral-Derivative (PID) controller to regulate the refrigerant mass flow rate. A stepwise validation was done with literature, wind tunnel tests, and chassis-dyno WLTP tests in a climatic cell. Theoretical heat transfer coefficients are obtained. The model accurately predicted the MAC energy. For an external temperature of 35 °C, MAC consumed around 950 Wh for BEVs, 790 Wh for the ICEV, and 760 Wh and 260 for the tested PHEVs in CD and CS modes, respectively, to reach a comfort temperature during a WLTC. The corresponding CO2 equivalent emissions for cooling are around 9.7, 36, 7.8, and 13.9, gCO2eq/km, respectively. Apart from the external temperature, the initial cabin temperature was an influencing parameter for MAC energy, as well as the seats masses and the PID controller gains. Cabin size showed negligible changes in MAC consumption.

The use of hydrogen to enrich or replace hydrocarbon-based flames represents an appealing perspective to reduce the emission of pollutants in gas turbine combustors. This implementation is, however, not straightforward, as the properties of hydrogen consistently differ from those of the most widely used fuels, resulting in a change in the combustion stability of the mixture. This work investigates the non-linear thermoacoustic stability of a simple Rijke tube containing a perfectly premixed, laminar, conical flame fed by either a pure methane-based mixture or a hydrogen-enriched one. The mean heat release rate and the geometry of the system were kept constant for both mixtures. Flame dynamics were modelled using a Level Set Method, from which a Flame Describing Function was derived for each mixture and coupled with a one-dimensional acoustic model within a low-order thermoacoustic framework to asses the stability of the system. The hydrogen-enriched flame was found to exhibit a weaker non-linear response than the methane-based flame, in agreement with previous observations reported in the literature. This behaviour was attributed to the higher unstretched laminar flame speed of the hydrogen-enriched mixture, which enhances the resilience of the flame front to develop geometric non-linearities following an acoustic perturbation, reducing its tendency towards heat release rate saturation. As a consequence, the hydrogen-enriched system generated larger thermoacoustic oscillations and ultimately experienced flashback, whereas the methane-based configuration remained bounded by more consistent non-linear saturation effects.

Permanent magnet synchronous motors (PMSMs) possess high power density and efficiency, which make them suitable for broad applications, including the transportation sector, thus contributing to its decarbonization. As demand for electric vehicles increases, the need for performance improvements on electric motors also rises. An essential requirement is efficient thermal management to prevent and mitigate overheating-related premature motor failure. Among the methods of thermal analysis, the Lumped Parameter Thermal Network (LPTN) model is distinguished by its versatility of applications and its low computational requirements, making it a proper tool for analyzing the temperature distribution within a motor. This paper focuses on radial PMSMs, emphasizing the identification of the primary heat sources and the corresponding heat transfer mechanisms involved. Furthermore, it provides a comprehensive literature review of LPTN models implemented by different authors, analyzing the formulations and highlighting the similarities and differences between the models presented in the existing literature.

Small centrifugal compressors are employed to boost automotive internal combustion engines or hydrogen fuel cells, amongst other applications. In many scopes, the overall intensity and quality of noise emission is a major concern, so many researchers develop and assess numerical models to predict acoustic spectra. In this work, Detached Eddy Simulations (DES) of a vaneless centrifugal compressor are conducted to assess the impact of intake geometries on its aeroacoustic performance. Experimental measurements are employed as a means of validation. Flow field analysis and Dynamic Mode Decomposition allow us to analyze the underlying mechanisms of the most relevant acoustic features. This work provides insight into the implications for noise generation of employing tight elbows due to packaging constraints.

This article presents an electro-thermal model of a prismatic lithium-ion cell, integrating physics-based models for capacity and resistance estimation. A 100 Ah prismatic cell with LFP-based chemistry was selected for analysis. A comprehensive experimental campaign was conducted to determine electrical parameters and assess their dependencies on temperature and C-rate. Capacity tests were conducted to characterize the cell’s capacity, while an OCV test was used to evaluate its open circuit voltage. Additionally, Hybrid Pulse Power Characterization tests were performed to determine the cell’s internal resistive-capacitive parameters. To describe the temperature dependence of the cell’s capacity, a physics-based Galushkin model is proposed. An Arrhenius model is used to represent the temperature dependence of resistances. The integration of physics-based models significantly reduces the required test matrix for model calibration, as temperature-dependent behavior is effectively predicted. The electrical response is represented using a first-order equivalent circuit model, while thermal behavior is described through a nodal network thermal model. Model validation was conducted under real driving emissions cycles at various temperatures, achieving a root mean square error below 1% in all cases. Furthermore, a comparative study of different cell cooling strategies is presented to identify the most effective approach for temperature control during ultra-fast charging. The results show that side cooling achieves a 36% lower temperature at the end of the charging process compared to base cooling.

The use of hydrogen to enrich or replace hydrocarbon-based flames represents an appealing perspective to reduce pollutant emissions in gas turbine combustors, but it involves significant issues related to their combustion stability. This paper investigates the linear stability of a Rijke tube fueled by two different conical, laminar, perfectly premixed flames: one based on a pure methane-air mixture and the other based on a pure hydrogen-air mixture. In both cases, the heat release rate was kept constant, while the other characteristics of the system were varied. Through the constant heat release rate, the inlet velocities of the mixtures, their Reynolds numbers and the flame aspect ratios were related to the radius of the chamber, which was limited within a realistic range in order to avoid both flashbacks and turbulent transition of the flow. A Flame Transfer Function was derived by means of a linearized Level Set Method approach, highlighting a neat disparity in the flame dynamics of the two mixtures. Linear stability of the system was assessed in order to understand its sensitivity to changes in the transversal dimension of the chamber, revealing clear differences in the behavior of hydrogen and methane mixtures, motivated by the dissimilarities in their gain response, and thus demonstrating a reduction in design flexibility of hydrogen-based combustion chambers.

The relation between Proper Orthogonal Decomposition (POD), Dynamic Mode Decomposition (DMD) and Flame Transfer Functions (FTF) is explored to gain further insight into the dynamics of two-dimensional laminar slit flames externally forced by velocity perturbations. The application of POD to the heat release rate fields suggests that the resultant modes can be split into two groups: the ones that are related to the displacement of the reactive sheet in the normal direction with regards to the flame front, and the ones that contain the local flame front distortions. The latter modes show preferential frequencies associated to the phase values of pi, 2 pi and 3 pi of the FTFs, which can be related to the maximum gain value depending on the case. Furthermore, the results of the modal analysis seem to support that the flame tip dynamics can be conceptually modelled as a set of standing waves whose joint response can reconstruct a propagation in the normal flame front direction, generating also the temporal fluctuations of the spatially-integrated heat release in the domain. The DMD analysis shows the existence of an interaction between the flame and the flow, and illustrates the fundamental role played by the velocity perturbations at the base for the motion of the reactive sheet. Thus, the analysis shows how data-based decomposition methods can be used to identify complex physical phenomena contained in the FTF graphs with different levels of detail, and extend the modal analysis to the physical space. Novelty and significance statement This paper proves the potential of modal decomposition techniques like POD and DMD to infer the underlying physics of canonical flames and their relation to the FTF, validating also the hypotheses and methodology of other reduced-order models in the literature. A canonical configuration featuring a twodimensional slit flame has been selected to analyse the flame dynamics using these methods. Hence, this work aims to set the foundations to elaborate data-driven reduced-order models based on modal decomposition techniques to support the analysis and the study of flame dynamics.

The study of topological states, which allow transport properties that are robust against impurities and defects in electronic structures, has been recently extended to the realm of elasticity. This work shows that nontrivial topological flexural edge states located on the free boundary of the elastic graphenelike metamaterial can be realized without breaking the time -reversal, mirror, or inversion symmetry of the system. Numerical calculations and experimental studies demonstrate the robust transport of flexural waves along the boundaries of the designed structure. The topological edge states on the free boundary are not limited by the size of the finite structure, which can reduce the scale of the topological state system. In addition, unlike the edge states localized on the free boundary in graphene where the group velocity is zero, the edge states on the elastic metamaterial plate have propagation states with nonzero group velocity. We have introduced the concept of Shannon entropy for elastic waves to assess the frequency range of the edge states in graphenelike elastic metamaterials. This work represents a relevant advance in the study of elastic wave topological states, providing a theoretical basis for engineering applications such as vibration reduction and vibration isolation for mechanical structures.

Lithium-ion batteries are highly affected by calendar ageing effects, which can lead to capacity loss even when the battery is not used at all. The current literature proposes plenty of semi-empirical models to predict the calendar ageing of the lithium-ion batteries, which are mostly based on Arrhenius functions for temperature dependency, exponential models for state of charge dependency and power laws for time dependency. Those models are easy to calibrate, and they provide a sufficiently precise prediction of capacity loss over time. However, it might be difficult to find a physical meaning to the parameters determined in these models, due to their lumped nature and the optimization process used. Therefore, in this work a semi-empirical model able to predict the capacity degradation over time with physically meaningful parameters is proposed. The dependency on temperature is considered by a pre-exponential factor whereas the dependency on state of charge is considered by a power law coefficient. A two-step constrained optimization process is considered to calibrate the model parameters. The model allows to predict the capacity loss over a wide range of different temperature and state of charge conditions, and it is calibrated for 4 different cell chemistries: LMO-NMC, LFP, NCA, and NMC. It was found that the worst storing condition is given by the highest temperature and state of charge conditions. The capacity degradation is provided over a period of 50 years. Two end-of-life conditions were analyzed: a loss of capacity of 20 % as representative of an end-of-life condition for automotive applications, and a loss of capacity of 50 % as an end-of-life condition for deep-space applications. In both cases, it was observed that NMC provided the best performance (the slowest ageing over time) for storing temperatures below 15 degrees C and storing state of charge below 10 %, whereas for temperatures higher than 15 degrees C and state of charge higher than 10 %, the LFP chemistry resulted to be the most longevous.

Adequate cell temperature estimation in lithium-ion batteries becomes crucial for state of charge (SOC) observation and safety purposes. The diagnosis of individual cell temperature in multicell battery packs depends on the number of temperature sensors available and the thermal dynamics of the system. This paper explores the potential of single value decomposition (SVD) of thermal distribution on battery packs in order to retain the critical information and minimize the number of states, but also the number of sensors required. The algorithm proposes a thermal lumped model to identify the thermal dynamics of the pack under different control actions and atmospheric conditions, and uses a Kalman filter to update the model states with temperature readings. Experimental tests were carried out to in a prototype with 20 cylindrical 21700 Li-Ion cell, equipped with several thermocouples and recording thermal images every 5 s. Results show that combining SVD with dynamic models and observers, the temperature distribution of the pack can be predicted with negligible errors.

Noise has been a much-studied problem since the beginning of aviation because it is one of the main factors affecting its social acceptance. In recent years, the expansion of Unmanned Aerial Vehicles (UAVs) has led to increased research on propellers working at low Reynolds numbers, which are typically found in this type of aircraft. This paper begins with an Improved Delayed Detached Eddy Simulation (IDDES) of a commercial nine-inch UAV propeller. The Ffowcs-Williams and Hawkings (FW-H) approach is then used to compute the acoustic propagation from the simulation results. Both aerodynamic performance and acoustic signature results in hover flight are validated experimentally. Then, the influence of different FW-H permeable surfaces is methodologically evaluated, finding that magnified SPL values at low frequencies are obtained when using cylinders due to vortical structures passing through the bottom cap, and that the use of spheres appears to be the most consistent approach. Once a validated, time-resolved flow field has been obtained, different data-driven modal decomposition techniques, such as Proper Orthogonal Decomposition (POD) and Dynamic Mode Decomposition (DMD), are applied to the 3D pressure field. This enables a better understanding of the propeller acoustic modes and the assessment of the suitability of each technique for this problem, especially when Reduced Order Models (ROMs) are sought.

Surface heat exchangers that use the bypass flow as heat sink are becoming a widely used solution to alleviate the high thermal load of modern aeroengines. Experimental characterization of such heat exchangers in full-scale engine tests is extremely expensive and time-consuming, so carrying out experiments in scaled wind tunnels that can replicate their very challenging flow conditions is highly desirable. However, intrusive instrumentation can affect the actual aerodynamics of the component due to the reduced size of the section and the typical high air velocities of turbofan engines. For this reason, a methodology to characterize a surface heat exchanger designed to work in the turbofan bypass using optical techniques is presented in this work. Additionally, the possibility of replicating the experimental conditions using additively manufactured models of exchangers would allow rapid preliminary characterization of these components. In this investigation, different non-intrusive techniques such as PIV, LDA, Schlieren, or LDV have been applied to determine the heat exchanger aerodynamic performance and vibration response, and a detailed characterization of the flow field has been carried out. Data have been cross-validated using different measurement techniques. A 3D-printed model has also been built to compare with the aluminum heat exchanger, showing almost an identical behavior in terms of velocity distribution downstream of the heat exchanger and pressure drop induced by its fins, as well as corrected frequencies, confirming thus the suitability of additive manufacturing for the aerodynamic characterization of these devices in preliminary stages.

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.

In this study, a methodology for the energy analysis of a lithium-ion battery module cooled by a serpentine cooling plate is proposed. A novel lumped electro-thermal model of a cooled module is calibrated and validated: thermal nodes are assigned to the Li-ion cells, the cooling plate, the thermal pad, and the coolant. The model is experimentally characterized and validated, and a maximum root mean square error equal to 1.44 % for the electrical model is obtained; all the errors of the thermal models are kept below the 2 %. The proposed approach allows to identify, with a low computational cost and reduced calculation time, the thermal evolution of the nodes depending on the environmental and operating conditions considered. This aspect is of fundamental importance to identify hot spots in the module and to prevent possible dangerous events such as thermal runaway. To highlight these advantages, an extended fast-charging parametric study of the module is carried out, considering 240 simulations, varying 4 parameters (ambient temperature, required electric power, temperature and coolant volumetric flow) and monitoring 3 variables (peak temperature in the module at the end of the charging process, thermal gradient, and time spent in the optimal temperature range), allowing to identify the combinations of operating parameters that permit the rapid charging of the module under optimal conditions. Furthermore, the energy analysis provides an estimation of the charging efficiency of the cells, which is around 90 % for every considered thermal boundary. The heat generated by the cells, the heat dissipated into the coolant and the heat absorbed by the other module components are estimated. In a 4C charge 80 % of total heat is dissipated into the coolant, while this quantity increases to 95 % in a 1C charge. The reduced computational time and cost make this model suitable both for cooling system design and for control strategies development.

The implementation of Surface Air-Cooled Oil Coolers (SACOCs) in new generation of turbofans is becoming more and more common. The main reason behind this is the need of improving the engine thermal management system as lubricants and components are working under increasingly severe demands. The idea of using the cold air from the bypass duct as a cold sink not only can be beneficial to cool down the oil, but because the enthalpy can be released into this flow to enhance the propulsion. However, it is extremely important to maintain the aerodynamic impact of the heat exchanger to its minimum so the propulsive efficiency is not worsened. In this investigation, the effect of rounding the sharp edges of a finned heat exchanger working under realistic conditions of very high Reynolds numbers is addressed. Firstly, a parametric study to determine the blend radius was numerically conducted and then, both geometries were experimentally tested for different Reynolds. The rounded version presented not only better results in terms of heat exchange, but also a considerable reduction in the aerodynamic impact of the fins. A detailed numerical analysis of both cases under nominal conditions showed that rounding the sharp edges enables the airflow to remain better attached to the fins, decreasing thus the aerodynamic impact in terms of velocities and vorticity and improving the thermal performances.

This work proposes a novel approach for state of health estimation of lithium-ion cells by developing a capacity fade model with temperature and Ah throughput dependencies. Two accelerated life cycle testing datasets are used for model calibration: a multi discharge rate dataset of an NMC/graphite cylindrical cell and a multi temperature dataset for an LCO/graphite pouch cell. The multi discharge rate dataset has been recorded at 23 °C and for 4 discharge-rates (C/4, C/2, 1C and 3C). The multi-temperature dataset considers the accelerated ageing of the cells at 4 temperatures (10, 25, 45 and 60 °C). An Arrhenius model is chosen for describing the temperature dependency while a power law model is chosen for cycle (Ah throughput) dependency. The model shows a good agreement with experimental data in each analyzed condition, allowing a precise description of the capacity degradation over time. From the single-temperature analysis, it is found that the activation energy decreases with respect to the C-rate: this is due to the fact that at higher C-rates, the irreversible chemical phenomena accelerate, leading to an overall faster ageing of the cell. From the multi-temperature analysis, the power law coefficient shows a quadratic dependency relative to temperature: a minimum for the power law coefficient is found corresponding to 25 °C, due to the fact that both for lower and higher temperatures, the ageing mechanisms are accelerated. Finally, an analysis of the impact of fast charging on cell ageing, in different charging scenarios is provided: the fast degradation of the cells at very low temperatures highlights the importance of an appropriate cooling of the battery during charging operations. This empirical methodology can be easily integrated in battery management system algorithms due to the easiness of the calibration and the low calculation time.

This article proposes a novel methodology for the definition of an optimized immersion cooling fluid for lithium-ion battery applications aimed to minimize maximum temperature and temperature gradient during most critical battery operations. The battery electric behavior is predicted by a first order equivalent circuit model, whose parameters are experimentally determined. Thermal behavior is described by a nodal network, assigning to each node thermal characteristics. Hence, the electro-thermal model of a battery is coupled with a thermal management model of an immersion cooling circuit developed in MATLAB/Simulink. A first characterization of the physical properties of an optimal dielectric liquid is obtained by means of a design of experiment. The optimal values of density, thermal conductivity, kinematic viscosity, and specific heat are defined to minimize the maximum temperature and temperature gradient during a complete discharge of the battery at 2.5C. Through a statistical analysis, it is also possible to recognize which effects among those previously mentioned are statistically relevant for this analysis. With the optimized fluid, a second design of experiment is carried out to define an optimized design of the module (in terms of distance between cells, and staggered angle), in relation to the operating conditions (volumetric flow and discharge rate). Once the optimal design has been identified, a final comparative study is carried out between different fluids used in immersion cooling systems, whose characteristics have been found in the literature, to find which of the fluids analyzed comply with the maximum temperature and maximum gradient conditions set for this study.

Due to the climate crisis and the restriction measures taken in the last decade, electric buses are gaining popularity in the transport sector. However, one of the most significant disadvantages of this type of vehicle is its low autonomy. Many electric buses with proton-exchange membrane fuel cells (PEMFC) systems have been developed to solve this problem in recent years. These have an advantage over battery-electric buses because the autonomy depends on the capacity of the hydrogen tanks. As with batteries, thermal management is crucial for fuel cells to achieve good performance and prolong service life. For this reason, it is necessary to investigate different strategies or configurations of a fuel cell electric bus's integral thermal management system (ITMS). In the present work, a novel global model of a fuel cell electric bus (FCEB) has been developed, which includes the thermal models of the essential components. This model was used to evaluate different strategies in the FCEB integrated thermal management system, simulating driving cycles of the public transport system of Valencia, Spain, under winter weather conditions. The first strategy was to use the heat generated by the fuel cell to heat the vehicle's cabin, achieving savings of up to 7%. The second strategy was to use the waste heat from the fuel cells to preheat the batteries. It was found that under conditions where a high-power demand is placed on the fuel cell, it is advisable to use the residual heat to preheat the battery, resulting in an energy saving of 4%. Finally, a hybrid solution was proposed in which the residual heat from fuel cells is used to heat both the cabin and the battery, resulting in an energy saving of 10%.

Energy management in electrified vehicles is critical and directly impacts the global operating efficiency, durability, driveability, and safety of the vehicle powertrain. Given the multitude of components of these powertrains, the complexity of the proper control is significantly higher than the conventional internal combustion engine vehicle (ICEV). Hence, several control algorithms and numerical methods have been developed and implemented in order to optimize the operation of the hybrid powertrain while complying with the required boundary conditions. In this work, a model-based method is used for predicting the impacts of a set of possible control actions, choosing the one minimizing the associated costs. In particular, the energy management technique used in the present study is the equivalent consumption minimization strategy (ECMS). The novelty of this work consists of taking into account the thermal state of the ICE for optimization. This feature was implemented by means of an extensive experimental campaign at different coolant temperatures of the ICE to calibrate the additional fuel consumption due to operating the engine outside of its optimum temperature. The results showed significant gains in both WLTC and RDE cycles.

Most of the equivalent circuit battery models available in the literature have been developed specifically for one cell and require extensive measurements to calibrate cell electrical parameters in different operating conditions. In this work, a generalized equivalent circuit model for lithium-iron phosphate batteries is proposed, which only relies on the nominal capacity, available in the cell datasheet. Using data from cells previously characterized, a generalized zeroth-order model is developed. This novel approach allows to avoid time-consuming and expensive experiments and reduces the test matrix. In spite of not relying on detailed data on the dependence of the electrical parameters with respect to state of charge, c-rate and temperature, the model provides an excellent description of the electrical behavior for both low-energy and high-energy cells, the error being always kept below 2 %. The internal resistance of the cell is expressed as a function of a new characteristic coefficient, which is typical of this lithium-ion battery chemistry. This coefficient is fitted to an exponential function of the temperature, which is physically meaningful, as the internal resistance has an Arrhenius-like behavior with respect to temperature. This model, due to its simplicity and flexibility, is particularly useful for control-oriented applications, and for off-line analyses.