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To prevent global warming, further reductions in carbon dioxide are required. It is therefore important to promote the spread of electric vehicles powered by internal combustion engines and electric vehicles without internal combustion engines. As a result, emissions from hybrid electric vehicles equipped with internal combustion engines should be further reduced. Interest in catalytic reactions in an electric field with a higher catalytic activity compared to conventional catalysts has increased because this technology consumes less energy than other electrical heating devices. This study was therefore undertaken to apply a catalytic reaction in an electric field to an exhaust emission control. First, the original experimental equipment was built with a high voltage system used to conduct catalytic activity tests.

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.

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'.

In a Diesel Oxidation Catalyst (DOC) and Catalyzed Soot Filter (CSF) system, the DOC is used to oxidize additional fuel injected into the cylinder and/or exhaust pipe in order to increase the CSF's inlet temperature during soot regeneration. The catalyst's hydrocarbon (HC) oxidation performance is known to be strongly affected by the HC species present and the catalyst design. However, the engine operating conditions and additive fuel supply parameters also affect the oxidation performance of DOCs, but the effects of these variables have been insufficiently examined. Therefore, in this study, the oxidation performance of a DOC was examined in experiments in which both exhaust gas recirculation (EGR) levels and exhaust pipe injection parameters were varied. The results were then analyzed and optimal conditions were identified using modeFRONTIER.

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.

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.

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.

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.

For diesel emission control systems containing a Diesel Oxidation Catalyst (DOC) and a Catalyzed Soot Filter (CSF) the DOC is used to oxidize the additional fuel injected into the cylinder and/or the exhaust pipe for the purpose of increasing the CSF inlet temperature during the soot regeneration. Hydrocarbon (HC) oxidation performance of the DOC is affected by HC species as well as a catalyst design, i.e., precious metal species, support materials and additives. How engine-out HC species vary as a function of fuel supply conditions is not well understood. In addition, the relationship between catalyst design and oxidation activity of different hydrocarbon species requires further study. In this study, diesel fuel was supplied by in-cylinder, post injection and exhaust HC species were measured by a gas chromatograph-mass spectrometry (GC-MS) and a gas analyzer. The post injection timing was set to either 73°, 88° or 98° ATDC(after top dead center).

There are various standards to evaluate the quality of software. Tools to quantatively evaluate the quality of software have become available in recent year. Although these tools are effective, warning reports can become extensive, when the volume of software becomes large. And, the manpower to confirm the report also becomes large. Knowledge and experience are to analyze the warning report. Consequently, an oversight, a misapprehension, etc. may arise. To solve this problem, we are examining system to automate this work.

A urea SCR system was combined with a DPF system to reduce NOx and PM in a four liters turbocharged with intercooler diesel engine. Significant reduction in NOx was observed at low exhaust gas temperatures by increasing NH3 adsorption quantity in the SCR catalyst. Control logic of the NH3 adsorption quantity for transient operation was developed based on the NH3 adsorption characteristics on the SCR catalyst. It has been shown that NOx can be reduced by 75% at the average SCR inlet gas temperature of 158 deg.C by adopting the NH3 adsorption quantity control in the JE05 Mode.

In this paper, we consider smart charging and vehicle-to-home (V2H) technologies for plugin electric vehicles coordinated with home energy management systems (HEMS) for automated demand response. In this system, plugin electric vehicles automatically react to demand response events with or without HEMS’s coordination, while vehicles are charged and discharged (i.e., V2H) in appropriate time slots by taking into account demand response events, time-ofuse rate information, and users’ vehicle usage plan. We introduce three approaches on home energy management: centralized energy control, distributed energy control, and coordinated energy control. We implemented smart charging and V2H systems by employing two sets of standardized communication protocols: one using OpenADR 2.0b, SEP 2.0, and SAE standards and the other using OpenADR 2.0b, ECHONET Lite, and ISO/IEC 15118.

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.

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.

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.

An experimental ultra lightweight compact vehicle named “the Waseda Future Vehicle” has been designed and developed, aiming at a simultaneous achievement of low exhaust gas emissions, high fuel economy and driving performance. The vehicle is powered by a dual-type hybrid system having a SI engine, electric motor and generator. A high performance lithium-ion battery unit is used for electricity storage. A variety of driving cycles were reproduced using the hybrid vehicle on a chassis dynamometer. By changing the logics and parameters in the electronic control unit (ECU) of the engine, a significant improvement in emissions was possible, achieving a very high fuel economy of 34 km/h at the Japanese 10-15 drive mode. At the same time, a numerical simulation model has been developed to predict fuel economy. This would be very useful in determining design factors and optimizing operating conditions in the hybrid power system.

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.

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 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.

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.