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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.

For a quick reach to the operating temperatures, the three way catalytic converter is recently located closer to the engine and subjected to higher temperatures than before. At the same time, the three way catalytic converter has upper thermal limits. Therefore, the operating temperatures have to be estimated accurately in the early period of product development. In this research, the four analysis methods are linked with the one-dimensional engine cycle simulation to achieve the goals. Firstly, for the estimation of gas temperatures at the exhaust port of the engine, the combustion analysis using the 3D-CFD was conducted to accurately simulate the way the heat was generated. Then, for the estimation of heat dissipation from the exhaust system to the atmosphere, the heat conduction analysis coupled with the air flow analysis around the vehicle body using the 3D-CFD was conducted.

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.

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.

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.

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.

The NOx-reducing activity of a Cu-chabazite selective catalytic reduction (SCR) catalyst was analyzed over a wide temperature range. The analysis was based on the ammonia SCR (NH3-SCR) mechanism and accounted for Cu redox chemistry and reactions at Brønsted acid sites. The reduction of NOx to N2 (De-NOx) at Cu sites was found to proceed via different paths at low and high temperatures. Consequently, the rate-limiting step of the SCR reaction at Cu sites varied with the temperature. The rate of NOx reduction at Cu sites below 200°C was determined by the rate of Cu oxidation. Conversely, the rate of NOx reduction above 300°C was determined by the rate of NH3 adsorption on Cu sites. Moreover, the redox state of the active Cu sites differed at low and high temperatures. To clarify the role of the chabazite Brønsted acid sites, experiments were also performed using a H-chabazite catalyst that lacks Cu sites.

Three-way catalyst (TWC) converters are used to remove harmful substances (e.g., carbon monoxide (CO), nitrogen oxides (NOx), and hydrocarbons (HC)) emitted from gasoline engines. However, a large amount of emissions could be emitted before the TWC reaches its light-off temperature during a cold start. For hybrid electric vehicles (HEVs) powered by gasoline engines, the emission purification performance by TWC converters unfortunately deteriorates because of mode switching from engine to battery and vice versa, which can repeatedly generate cold start conditions for the TWCs. In this study, aiming to reduce emissions from series HEVs by early activation of TWCs, numerical simulations and experiments are carried out. An HEV is tested on a chassis dynamometer in the Worldwide Light-duty Test Cycle (WLTC) mode. The upstream and downstream gas conditions of the close-coupled catalyst converter are measured.

Pre-chamber spark ignition technology can stabilize combustion and improve thermal efficiency of lean burn natural gas engines. During compression stroke, a homogeneous lean mixture is introduced into pre-chamber, which separates spark plug electrodes from turbulent flow field. After the pre-chamber mixture is ignited, the burnt jet gas is discharged through multi-hole nozzles which promotes combustion of the lean mixture in the main chamber due to turbulence caused by high speed jet and multi-points ignition. However, details mechanism in the process has not been elucidated. To design the pre-chamber geometry and to achieve stable combustion under the lean condition for such engines, it is important to understand the fundamental aspects of the combustion process. In this study, a high-speed video camera with a 306 nm band-pass filer and an image intensifier is used to visualize OH* self-luminosity in rapid compression-expansion machine experiment.

Ammonia-selective catalytic reduction (SCR) systems have been introduced commercially in diesel vehicles, however catalyst systems with higher conversion efficiency and better control characteristics are required to know the actual emissions during operation and the emissions in random test cycles. Computational fluid dynamics (CFD) is an effective approach when applied to SCR catalyst development, and many models have been proposed, but these models need experimental verification and are limited in the situations they apply to. Further, taking account of redox cycle is important to have better accuracy in transient operation, however there are few models considering the cycle. Model development considering the redox reactions in a zeolite catalyst, Cu-ZSM-5, is the object of the research here, and the effects of exhaust gas composition on the SCR reaction and NH3 oxidation at high temperatures are investigated.

Experimental studies were carried out to investigate the effect of soot deposition on NOx purification phenomena in an ammonia selective catalytic reduction coated diesel particulate filter (SCR/DPF) catalyst. To study soot deposition effects on the chemical reactions and mass transfer, two types of testing device were used. A synthetic gas bench enabling tests to be conducted with temperature and flow rate ranges relevant to real driving conditions was used to investigate the soot influence on reduction of NOx to N2 (DeNOx). A micro-reactor that removed the effect of soot deposition on mass transfer in the catalyst layer was used to analyze chemical reactions on a soot surface and their interaction with the SCR catalyst. A filter test brick of a Cu-zeolite SCR/DPF catalyst and a powder catalyst were used for the synthetic gas bench and micro-reactor tests, respectively. Engine soot was sampled in all the tests.

Experimental and numerical studies on the combustion of the particulate matter in the diesel particulate filter with the particulate matter loaded under different particulate matter loading condition were carried out. It was observed that the pressure losses through diesel particulate filter loaded with particulate matter having different mean aggregate particle diameters during both particulate matter loading and combustion periods. Diesel particulate filter regeneration mode was controlled with introducing a hot gas created in Diesel Oxidation Catalyst that oxidized hydrocarbon injected by a fuel injector placed on an exhaust gas pipe. The combustion amount was calculated with using a total diesel particulate filter weight measured by the weight meter both before and after the particulate matter regeneration event.

Experimental and numerical studies were conducted on diesel particulate filters (DPFs) under different soot loading conditions and DPF configurations. Pressure drops across DPFs with various mean pore diameters loaded with soots having different mean particle diameters were measured by introducing exhaust gases from a 2.2 liter inline four-cylinder, TCI diesel engine designed for use in passenger cars. A mechanistic hypothesis was then proposed to explain the observed trends, accounting for the effects of the soot loading regime in the wall and the soot cake layer on the pressure drop. This hypothesis was used to guide the development and validation of a numerical model for predicting the pressure drop in the DPF. The relationship between the permeability and the porosity of the wall and soot cake layer was modeled under various soot loading conditions.

The objective of this paper is to investigate the potential of lean burn combustion to improve the thermal efficiency of spark ignition engine. Experiments used a single cylinder gasoline spark ignition engine fueled with primary reference fuel of octane number 90, running at 4000 revolution per minute and at wide open throttle. Experiments were conducted at constant fueling rate and in order to lean the mixture, more air is introduced by boosted pressure from stoichiometric mixture to lean limit while maintaining the high output engine torque as possible. Experimental results show that the highest thermal efficiency is obtained at excess air ratio of 1.3 combined with absolute boosted pressure of 117 kPa. Three dimensional computational fluid dynamic simulation with detailed chemical reactions was conducted and compared with results obtained from experiments as based points.