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A major issue of battery electric vehicles (BEV) is optimizing driving range and energy consumption. Under actual driving, transient thermal and electrical performance changes could deteriorate the battery cells and pack. These performances can be investigated and controlled efficiently with a thermal management system (TMS) via model-based development. A complete battery pack contains multiple cells, bricks, and modules with numerous coolant pipes and flow channels. However, such an early modeling stage requires detailed cell geometry and specifications to estimate the thermal and electrochemical energies of the cell, module, and pack. To capture the dynamic performance changes of the LIB pack under real driving cycles, the thermal energy flow between the pack and its TMS must be well predicted. This study presents a BTMS model development and validation method for a 75-kWh battery pack used in mass-production, mid-size battery SUV under WLTC.

The recent and future trends of energy for heavy-duty vehicles are considered e-fuel, H2, and electricity, and the Selective Catalytic Reduction (SCR) system is necessary for achieving the goals of zero-emission internal combustion engines that use e-fuel and H2 as a fuel. The Japanese automotive industry uses a Cu-zeolite based SCR catalyst since Vanadium is designated as a specific chemical substance, which the Ministry of Environment prohibits its release into the atmosphere. This study attempted purification rate improvement by controlling the NH3 supply with a mini-reactor and by simulated exhaust gas. Specifically, the experiment was done by examining the effect of the pulse amplitude, frequency, and duty ratio on the purification rate by supplying the NH3 pulse injection to the test piece Cu-chabazite catalyst. Additionally, the results of the reactor experiment were validated by numerical simulation considering the detailed surface reaction processes on the catalyst.

Performances of battery electric vehicles (BEV) are affected by the thermal imbalance in the battery packs under driving cycles. BEV thermal management system (VTMS) should be managed efficiently for optimal energy consumption and cabin comfort. Temperature changes in the brick, module, and pack under the repeated transient cycles must be understood for model-based development. The authors conducted chassis dynamometer experiments on a fully electric small crossover sports utility vehicle (SUV) to address this challenge. A BEV is tested using a hub-type, 4-wheel motor chassis dynamometer with an air blower under the Worldwide Harmonized Light Vehicles Test Cycle (WLTC) and Federal Test Procedures (FTP) with various ambient temperatures. The mid-size BEV with dual-motor featured 80 thermocouples mounted on the 74-kWh battery pack, including the cells, upper tray, side cover, and pack cover.

Battery thermal management system (BTMS) significantly improves battery electric vehicle (BEV) performance, especially under cold weather. A tradeoff between battery power consumption and cabin heating performance must be considered in cold driving conditions. This preliminary study aims to develop an integrated battery pack and coolant channel model to predict the thermal behavior of a BEV thermal management system. In this study, we develop and calibrate the physical baseline model with testbench data using finite element and CFD software. First, an electrochemical battery cell 1D model (Pseudo-2D or P2D) is built and calibrated against the cell reference data. An integrated pack model consisting of four modules (each has 23 and 25 bricks and a total of 4416 cells) with coolant flow channels is developed using reference and benchmarked data.

Alternative propulsion systems such as renewable fuels and electric powertrains are expensive; thus, efficient internal combustion engines (ICE) with hybrid powertrains still play significant roles in the transportation fleet in the coming decades. Modern engine technologies have been adopted to meet stringent emissions and fuel economy standards. As a result, engine control systems are becoming more complex. Furthermore, as ICE control parameters increase exponentially, engine calibration and design become bottlenecks in the development process. While a map-based feed-forward control method is a current de facto standard in combustion control, online closed-loop feedback control can improve engine performance and robustness. However, adding physical sensors to measure the various data for the online feedback control and calibration increase the vehicle cost.

For a series hybrid electric vehicle (SHEV), the electric motor is responsible for driving the wheels, while the engine drives the only generator to provide electricity. SHEVs set a control strategy to make the engine run near the fixed operating point with high thermal efficiency, thereby effectively reducing fuel consumption. The powertrain system of HEV is more complex than that of a conventional drive system using only an internal combustion engine, and it is time-consuming to obtain the optimal components specification values and control parameters. Therefore, automatic optimization methods are required nowadays. We used Genetic Algorithm (GA) as the optimization method and optimize powertrain specifications and control parameter values to reduce fuel consumption. The results show that it is an effective optimization method.

Among industrial engines, vortex chamber diesel engines are mainly used in small engines with output of less than 19 kW, and they employ an indirect injection system in which fuel is injected into a sub-chamber called a vortex chamber. The throttle-type nozzle used in swirl-chamber diesel engines is expected to change its spraying behavior depending on ambient conditions because the pressure fluctuations in the nozzle cause the needle valve to lift, and the injection amount is controlled by the amount of lift of the needle valve. In addition, the dimensions of the vortex chamber of a vortex chamber diesel engine are smaller than the spray development distance, and wall impingement of the spray is expected. In this study, spraying and combustion experiments were conducted using a constant volume chamber to understand the behavior of the spray from a throttle-type nozzle.

The purpose of the present study is to find a way to extend a combustion stability limit for diluted combustion in a spark-ignition (SI) gasoline engine which has a high compression ratio. This paper focuses on partial oxidation in an unburned mixture which is observed in the high compression engine and clarifies the effect of partial oxidation in an unburned mixture on the behavior of a flame stretch and the extinction limit. The behavior of the flame stretch was simulated using the detailed chemical kinetics simulation with the opposed-flow flame reactor model. In the simulation, the reactants which have various reaction progress variables were examined to simulate the flame stretch and extinction under the partial oxidation conditions. The mixtures were also diluted by complete combustion products which represent exhaust gas recirculation (EGR).

This paper describes the development of an integrated simulation model for evaluating the effects of electrically heating the three-way catalyst (TWC) in a series hybrid electric vehicle (s-HEV) on fuel economy and exhaust gas purification performance. Engine and TWC models were developed in GT-Power to predict exhaust emissions during transient operation. These models were validated against data from vehicle tests using a chassis dynamometer and integrated into an s-HEV model built in MATLAB/Simulink. The s-HEV model accurately reproduced the performance characteristics of the vehicle’s engine, motor, generator, and battery during WLTC mode operation. It can thus be used to predict the fuel consumption, emissions, and performance of individual powertrain components. The engine combustion characteristics were reproduced with reasonable accuracy for the first 50 combustion cycles, representing the cold-start condition of the driving mode.

To reproduce wall quenching phenomena using 3D-CFD, a wall quenching model was constructed based on the Peclet number. The model was further integrated with the flame propagation model. Combustion analysis showed that that a large amount of unburned hydrocarbons (UHCs) remained in the piston clevis and small gaps. Furthermore, the model was capable of predicting the increase in UHC emissions when there was a delay in the ignition time. The flame front cells were plotted on Peters' premixed turbulent combustion diagram to identify transitions in the combustion states. It was found that the flame surface transitioned from corrugated flamelets through thin reaction zones to wrinkled flamelets and further to laminar flamelets, which led to wall quenching. The turbulent Reynolds number (Re) decreased rapidly due to the increase in laminar flame speed and flame thickness and the decrease in turbulent intensity and turbulent scale.

In this study, reaction path analysis and modeling of NOx reduction phenomena by fast SCR reaction on a Cu-chabazite catalyst were conducted, considering the formation and decomposition of ammonium nitrate (NH4NO3). White crystals of NH4NO3 decompose at temperatures < 200 °C. Thus, the reaction behavior changes at 200 °C under fast SCR reaction conditions. NH4NO3 formation can occur on both Cu sites and Brønsted acid sites, which are active sites for NOx reduction in the Cu-chabazite catalyst, but it is unclear where NH4NO3 accumulates on the catalyst. Analyses using catalyst test pieces with different active sites were performed to estimate this accumulation. The results suggested that NH4NO3 accumulation does not depend on the presence of either Cu sites or Brønsted acid sites. Therefore, it is assumed that NH4NO3 can be accumulated everywhere on the catalyst, including on the zeolite framework. This phenomenon was included in the model as formation/accumulation sites S'.

Performance predictions of advanced turbocharged engines are becoming difficult because conventional engine models are built using performance map data of turbochargers with a proportional integral derivative (PID) controller. Improving prediction capabilities under transient test cycles or real driving conditions is a challenging task. This study applies a machine learning technique to predict turbocharger performances with high accuracy under steady-state and transient conditions. The manipulated signals of engine speed and torque created based on Compressed High-Intensity Radiated Pulse (Chirp signal) and Amplitude-modulated Pseudo-Random Binary Signal (APRBS) are used as inputs to the engine testbed. Data from the engine experiments are used as training data for the AI-based turbocharger model. High prediction accuracy of the AI turbocharger model is achieved with the co-efficient of determination in the model, and cross-validation results are higher than 0.8.

Emission regulations are becoming tighter, and Real Driving Emissions (RDE) is proposed as a testing cycle for evaluating modern engine emissions under a wide operation range. For this reason, engine manufacturers have been developing a method to effectively assess engine performances and emissions under a wide range of transient conditions. Transient engine performances can be evaluated efficiently by applying time-series data created by chirp signals. However, when the time-series data produced by the chirp signal are used directly, the engine hardware may damage, and emission performances deteriorate drastically. It is therefore essential to develop a method to avoid these undesirable operating conditions. This work aims to develop an algorithm to avoid such unrealistic operation conditions for engine performance evaluation. A virtual diesel engine (VDE) model is developed based on a four-cylinder engine using GT-POWER software.

Natural gas has been used in spark-ignition (SI) engines of natural gas vehicles (NGVs) due to its resource availability and stable price compared to gasoline. It has the potential to reduce carbon monoxide emissions from the SI engines due to its high hydrogen-to-carbon ratio. However, short running distance is an issue of the NGVs. In this work, methodologies to improve the fuel economy of a heavy-duty commercial truck under the Japanese Heavy-Duty Driving Cycle (JE05) is proposed by numerical 1D-CFD modeling. The main objective is a comparative analysis to find an optimal fuel economy under three variable mechanisms, variable valve timing (VVT), variable valve actuation (VVA), and variable compression ratio (VCR). Experimental data are taken from a six-cylinder turbocharged SI engine fueled by city gas 13A. The 9.83 L production engine is a CR11 type with a multi-point injection system operated under a stoichiometric mixture.

In this study, reaction path analysis and modeling of NOx reduction phenomena by selective catalytic reduction (SCR) with NH3 over a Cu-chabazite catalyst were conducted considering changes in the valence state of Cu sites and local structure due to differences in ligands to the Cu sites. The analysis showed that in the Cu-chabazite catalyst, NOx was mainly reduced by adsorbed NH3 on divalent Cu sites accompanied by a change in valence state of Cu from divalent to monovalent. It is known that the activation energy of NOx reduction on a Cu-chabazite catalyst changes between low temperatures ≤ 200 °C and mid to high temperatures ≥ 300 °C. To express this phenomenon, a reversible hydrolysis reaction based on the difference in coordination state of hydroxyl groups (OH−) to Cu sites at low and high temperatures was introduced into the model.

Heavy soot deposition in wall-flow type diesel particulate filters reduces engine output and fuel efficiency. This necessitates forced regeneration to oxidize soot via exothermic reactions in a diesel oxidation catalyst upstream of the Diesel Particulate Filter (DPF). Soot loading in the wall of the DPF during forced regeneration causes much greater pressure drops than cake deposition, which is undesirable because high pressure drops reduce engine performance. We show that the description of soot deposition using a DPF model is improved by using a shrinking sphere soot oxidation sub-model. We then use this revised model to analyze cake deposition during forced regeneration, and to study the effects of varying the forced regeneration temperature and duration on the local soot reaction rate and soot mass distribution in the radial and longitudinal directions of the DPF channels during forced regeneration.

Battery models are being developed as a component of the powertrain systems of hybrid electric vehicles (HEVs) to predict the state of charge (SOC) accurately. Electrically heated catalysts (EHCs) can be employed in the powertrains of HEVs to reach the catalyst light off temperature in advance. However, EHCs draw power from the battery pack and hence sufficient energy needs to be stored to power auxiliary components. In series HEVs, the engine is primarily used to charge the battery pack. Therefore, it is important to develop a control strategy that triggers engine start/stop conditions and reduces the frequency of engine operation to minimize the equivalent fuel consumption. In this study, a battery pack model was constructed in MATLAB-Simulink to investigate the SOC variation of a high-power lithium ion battery during extreme engine cold start conditions (-7°C) with/without application of an EHC.

To identify ways of achieving good mixture formation and heat release in diesel spray combustion, we have performed Large Eddy Simulation (LES) using a detailed chemical reaction mechanism to study the temporal and spatial distribution of the local equivalence ratios and heat release rate. Here we characterize the effect of the fuel injection rate profile on these processes in the combustion chamber of a diesel engine. Two injection rate profiles are considered: a standard (STD) profile, which is a typical modern common rail injection profile, and the inverse delta (IVD) profile, which has the potential to suppress rich mixture formation in the spray tip region. Experimental data indicate that the formation of such mixtures may extend the duration of the late combustion period and thus reduce thermal efficiency.

This paper describes a method for estimating constants related to NH3 gas diffusion phenomena to the active sites in a selective catalytic reduction diesel particulate filter (SCR/DPF) catalyst. A simple one-dimensional NH3 gas diffusion model based on the pore structure inside the catalyst was developed and used to estimate the intracrystalline diffusion coefficient. It was shown that the estimated value agreed well with experimental data.

Three-way catalyst (TWC) converters are often used to purify toxic substances contained in exhaust emissions from gasoline engines. However, a large amount of CO, NOx and THC may be emitted before the TWC reaches its light-off temperature during a cold start. In this work, a numerical model was developed for studying the purification performance of a close-coupled TWC converter during the cold start period. The TWC model was built using axisuite, commercial software by Exothermia S.A. Model gas experiments were designed for calibrating the chemical reaction scheme and corresponding reaction rate parameters in the TWC model. The TWC model was able to simulate the purification performance of CO, NOx and THC under both lean and rich air-fuel equivalence ratios (λ) for different conditions. The light-off temperature and oxygen storage capacity (OSC) behavior were also successfully validated in the model. Vehicle tests were conducted on a chassis dynamometer to verify the TWC model.