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Thermal barrier coatings (TBCs) have been of interest since the 1970s for application in internal combustion (IC) engines. Thin TBCs exhibit a temperature swing phenomenon wherein wall temperatures dynamically respond to the transient working-gas temperature throughout the engine cycle, thus reducing the temperature difference driving the heat transfer. Determining these varying wall temperatures is necessary to evaluate and study the effect of coatings on wall heat transfer. This study focuses on developing a 3D computational fluid dynamics (CFD)-finite element analysis (FEA) coupled simulation, or co-simulation, routine to determine the wall temperatures of a piston coated with a thin TBC layer subject to spark ignition combustion heat flux. A CONVERGE 3D-CFD model was used to simulate the combustion process in a single-cylinder, light-duty experimental spark ignition (SI) engine.

Opposed-piston 2-stroke (OP-2S) engines have the potential to achieve higher thermal efficiency than a typical diesel engine. However, the uniflow scavenging process is difficult to control over a wide range of speeds and loads. Scavenging performance is highly sensitive to pressure dynamics, port timings, and port design. This study proposes an analysis of the effects of port vane angle on the scavenging performance of an opposed-piston 2-stroke engine via simulation. A CFD model of a three-cylinder opposed-piston 2-stroke was developed and validated against experimental data collected by Achates Power Inc. One of the three cylinders was then isolated in a new model and simulated using cycle-averaged and cylinder-averaged initial/boundary conditions. This isolated cylinder model was used to efficiently sweep port angles from 12 degrees to 29 degrees at different pressure ratios.

In this work, a novel opposed piston architecture is proposed where one crankshaft rotates at twice the speed of the other. This results in one piston creating a 2-stroke profile and another with a 4-stroke profile. In this configuration, the slower piston operates in the 2-stroke CAD domain, while the faster piston completes 2 reciprocating cycles in the same amount of time (4-stroke). The key benefit of this cycle is that the 4-stroke piston increases the rate of compression and expansion (dV/dθ), which lowers the combustion-induced pressure rise rate after top dead center (crank angle location of minimum volume). Additionally, it lowers in-cylinder temperatures and pressures more rapidly, resulting in a lower residence time at high temperatures, which reduces residence time for thermal NOx formation and reduces the temperature differential between the gas and the wall, thereby reducing heat transfer.

In-cylinder surface temperature is of heightened importance for Homogeneous Charge Compression Ignition (HCCI) combustion since the combustion mechanism is thermo-kinetically driven. Thermal Barrier Coatings (TBCs) selectively manipulate the in-cylinder surface temperature, providing an avenue for improving thermal and combustion efficiency. A surface temperature swing during combustion/expansion reduces heat transfer losses, leading to more complete combustion and reduced emissions. At the same time, achieving a highly dynamic response sidesteps preheating of charge during intake and eliminates the volumetric efficiency penalty. The magnitude and temporal profile of the dynamic surface temperature swing is affected by the TBC material properties, thickness, morphology, engine speed, and heat flux from the combustion process. This study follows prior work of authors with Yttria Stabilized Zirconia, which systematically engineered coatings for HCCI combustion.

Energy management of hybrid vehicle has been a widely researched area. Strategies like dynamic programming (DP), equivalent consumption minimization strategy (ECMS), Pontryagin’s minimum principle (PMP) are well analyzed in literatures. However, the adaptive optimization work is still lacking, especially for reinforcement learning (RL). In this paper, Q-learning, as one of the model-free reinforcement learning method, is implemented in a mid-size 48V mild parallel hybrid electric vehicle (HEV) framework to optimize the fuel economy. Different from other RL work in HEV, this paper only considers vehicle speed and vehicle torque demand as the Q-learning states. SOC is not included for the reduction of state dimension. This paper focuses on showing that the EMS with non-SOC state vectors are capable of controlling the vehicle and outputting satisfactory results. Electric motor torque demand is chosen as action.

The Organic Rankine Cycle (ORC) has proven to be a promising technology for Waste Heat Recovery (WHR) systems in heavy duty diesel engine applications. However, due to the highly transient heat source, controlling the working fluid flow through the ORC system is a challenge for real time application. With advanced knowledge of the heat source dynamics, there is potential to enhance power optimization from the WHR system through predictive optimal control. This paper proposes a look-ahead control strategy to explore the potential of increased power recovery from a simulated WHR system. In the look-ahead control, the future vehicle speed is predicted utilizing road topography and V2V connectivity. The forecasted vehicle speed is utilized to predict the engine speed and torque, which facilitates estimation of the engine exhaust conditions used in the ORC control model.

Most studies on supervisory controllers of hybrid electric vehicles consider only fuel economy in the objective function. Taking into consideration the importance of the energy storage system health and its impact on the vehicle’s functionality, cost, and warranty, recent studies have included battery degradation as the second objective function by proposing different energy management strategies and battery life estimation methods. In this paper, a rule-based supervisory controller is proposed that splits the torque demand based not only on fuel consumption, but also on the battery capacity fade using the concept of severity factor. For this aim, the severity factor is calculated at each time step of a driving cycle using a look-up table with three different inputs including c-rate, working temperature, and state of charge of the battery. The capacity loss of the battery is then calculated using a semi-empirical capacity fade model.

This paper presents the transient power optimization of an organic Rankine cycle waste heat recovery (ORC-WHR) system operating on a heavy-duty diesel (HDD). The optimization process is carried on an experimentally validated, physics-based, high fidelity ORC-WHR model, which consists of parallel tail pipe and EGR evaporators, a high pressure working fluid pump, a turbine expander, etc. Three different ORC-WHR mixed vapor temperature (MVT) operational strategies are evaluated to optimize the ORC system net power: (i) constant MVT; (ii) constant superheat temperature; (iii) fuzzy logic superheat temperature based on waste power level. Transient engine conditions are considered in the optimization. Optimization results reveal that adaptation of the vapor temperature setpoint based on evaporation pressure strategy (ii) provides 1.1% mean net power (MNP) improvement relative to a fixed setpoint strategy (i).

Low-temperature gasoline combustion (LTGC) has the potential to provide gasoline-fueled engines with efficiencies at or above those of diesel engines and extremely low NOx and particulate emissions. Three key performance goals for LTGC are to obtain high loads, reduce the boost levels required for these loads, and achieve high thermal efficiencies (TEs). This paper reports the results of an experimental investigation into the use of partial fuel stratification, produced using early direct fuel injection (Early-DI PFS), and an increased compression ratio (CR) to achieve significant improvements in these performance characteristics. The experiments were conducted in a 0.98-liter single-cylinder research engine. Increasing the CR from 14:1 to 16:1 produced a nominal increase in the TE of about one TE percentage unit for both premixed and Early-DI PFS operation.

This paper presents the development of an electrochemical aging model of LiFePO4-Graphite battery based on single particle (SP) model. Solid electrolyte interphase (SEI) growth is considered as the aging mechanism. It is intended to provide both sufficient fidelity and computational efficiency required for integration within the HEV power management optimization framework. The model enables assessment of the battery aging rate by considering instantaneous lithium ion surface concentration rather than average concentration, thus enhancing the fidelity of predictions. In addition, an approximate analytical method is applied to speed up the calculation while preserving required accuracy. Next, this aging model are illustrated two applications. First is hybrid electric powertrain system model integration and simulation.

This paper presents an Organic Rankine Cycle (ORC) system model for heavy-duty diesel (HDD) applications. The dynamic, physics-based model includes: heat exchangers for parallel exhaust and EGR circuits, compressible vapor working fluid, distribution and flow control valves, a high pressure pump, and a reservoir. A finite volume method is used to model the evaporator, and a pressure drop model is included to improve the accuracy of predictions. Experimental results obtained on a prototype ORC system are used for model calibration and validation. Comparison of predicted and measured values under steady-state conditions is pursued first, followed by the analysis of selected transient events. Validation reveals the model’s ability to track real-world temperature and pressure dynamics of the ORC system. Therefore, this modeling framework is suitable for future system design studies, optimization of ORC power generation, and as a basis for development of control-oriented ORC models.

This paper develops a methodology to optimize the supervisory controller for a heavy-duty series hybrid electric vehicle, with consideration of battery aging and cooling loss. Electrochemistrybased battery aging model is integrated into vehicle model. The side reaction, reductive electrolyte decomposition, is modeled to determine battery aging rate, and the thermal effect on this reaction rate is considered by Arrhenius Law. The resulting capacity and power fading is included in the system-level study. Sensitivity analysis shows that battery aging could cause fuel economy loss by 5.9%, and increasing temperature could improve fuel economy at any given state-of-health, while accelerating battery aging. Stochastic dynamic programming algorithm is applied to a modeled system to handle the tradeoff between two objectives: maximizing fuel economy and minimizing battery aging.

Commercial vehicles transport the majority of the inland freight in US and a significant number of passengers. They are large fuel consumers as they operate a large number of hours per day, pulling heavy loads. The increasing fuel price and the Green House Gas emission regulation have provided a strong impetus for new technologies capable of improving the commercial vehicle fuel economy. Among others, optimized powertrain control can improve the vehicle fuel economy, particularly if it is based on accurate information about the instantaneous load demand. Furthermore, model-based online vehicle parameter estimator is critical for implementation of an adaptive vehicle controller. While vehicle mass estimation has been successfully demonstrated, rolling resistance and aerodynamic drag estimation has not been fully explored yet. This paper examines this problem using model-based approach with a supervisory data extraction scheme.

Hydrogen can be produced by electrolyzation with renewable electricity and the combustion products of hydrogen mixture include no CO, CO2 and hydrocarbons. In this study, engine performance with hydrogen / diesel dual fuel (hydrogen DDF) operation in a multi-cylinder diesel engine is investigated due to clarify advantages and disadvantages of hydrogen DDF operation. Hydrogen DDF operation under several brake power conditions are evaluated by changing a rate of hydrogen to total input energy (H2 rate). As H2 rate is increased, an amount of diesel fuel is decreased to keep a given torque constant. When the hydrogen DDF engine is operated with EGR, Exhaust gas components including carbon are improved or suppressed to same level as conventional diesel combustion. In addition, brake thermal efficiency is improved to 40% by increase in H2 rate that advances combustion phasing under higher power condition. On the other hand, NOx emission is much higher than one of conventional diesel engine.

The limited operational range of low temperature combustion engines is influenced by near-wall conditions. A major factor is the accumulation and burn-off of combustion chamber deposits. Previous studies have begun to characterize in-situ combustion chamber deposit thermal properties with the end goal of understanding, and subsequently replicating the beneficial effects of CCD on HCCI combustion. Combustion chamber deposit thermal diffusivity was found to differ depending on location within the chamber, with significant initial spatial variations, but a certain level of convergence as equilibrium CCD thickness is reached. A previous study speculatively attributed these spatially dependent CCD diffusivity differences to either local differences in morphology, or interactions with the fuel-air charge in the DI engine. In this work, the influence of directly injected gasoline on CCD thermal diffusivity is measured using the in-situ technique based on fast thermocouple signals.

Hydrogen can be produced by electrolyzation with renewable electricity and reduce the combustion products from hydrogen mixture don't include CO, CO2 and unburned hydrocarbon components. We focused on these characteristics of hydrogen and high thermal efficiency of diesel engine and acquired the performance of hydrogen diesel dual fuel (DDF) engine. We changed proportion of hydrogen to total input energy and studied basic combustion and exhaust gas emission performance of hydrogen DDF operation. In addition, we studied the effects of advancement of diesel fuel injection timing and EGR on combustion behavior and improvement of NOx emission. Especially, EGR improved NOx emission from hydrogen DDF operation drastically without a decrease in thermal efficiency. Under hydrogen DDF operation with EGR, diesel fuel injection timing was advanced for stable combustion and it inhibited the degradation of thermal efficiency.

A detailed understanding of the various factors affecting the trends in gross-indicated thermal efficiency with changes in key operating parameters has been carried out, applied to a one-liter displacement single-cylinder boosted Low-Temperature Gasoline Combustion (LTGC) engine. This work systematically investigates how the supplied fuel energy splits into the following four energy pathways: gross-indicated thermal efficiency, combustion inefficiency, heat transfer and exhaust losses, and how this split changes with operating conditions. Additional analysis is performed to determine the influence of variations in the ratio of specific heat capacities (γ) and the effective expansion ratio, related to the combustion-phasing retard (CA50), on the energy split. Heat transfer and exhaust losses are computed using multiple standard cycle analysis techniques. The various methods are evaluated in order to validate the trends.

Low-temperature gasoline combustion (LTGC), based on the compression ignition of a premixed or partially premixed dilute charge, can provide thermal efficiencies (TE) and maximum loads comparable to those of turbo-charged diesel engines, and ultra-low NOx and particulate emissions. Intake boosting is key to achieving high loads with dilute combustion, and it also enhances the fuel's autoignition reactivity, reducing the required intake heating or hot residuals. These effects have the advantages of increasing TE and charge density, allowing greater timing retard with good stability, and making the fuel ϕ- sensitive so that partial fuel stratification (PFS) can be applied for higher loads and further TE improvements. However, at high boost the autoignition reactivity enhancement can become excessive, and substantial amounts of EGR are required to prevent overly advanced combustion.

This paper presents a new way to evaluate vehicle speed profile aggressiveness, quantify it from the perspective of the rapid speed fluctuations, and assess its impact on vehicle fuel economy. The speed fluctuation can be divided into two portions: the large-scale low frequency speed trace which follows the ongoing traffic and road characteristics, and the small-scale rapid speed fluctuations normally related to the driver's experience, style and ability to anticipate future events. The latter represent to some extent the driver aggressiveness and it is well known to affect the vehicle energy consumption and component duty cycles. Therefore, the rapid speed fluctuations are the focus of this paper. Driving data collected with the GPS devices are widely adopted for study of real-world fuel economy, or the impact on electrified vehicle range and component duty cycles.

The components in a hybrid electric vehicle (HEV) powertrain include the battery pack, an internal combustion engine, and the electric machines such as motors and possibly a generator. These components generate a considerable amount of heat during driving cycles. A robust thermal management system with advanced controller, designed for temperature tracking, is required for vehicle safety and energy efficiency. In this study, a hybridized mid-size truck for military application is investigated. The paper examines the integration of advanced control algorithms to the cooling system featuring an electric-mechanical compressor, coolant pump and radiator fans. Mathematical models are developed to numerically describe the thermal behavior of these powertrain elements. A series of controllers are designed to effectively manage the battery pack, electric motors, and the internal combustion engine temperatures.