Browse Topic: Environmental regulations and standards
After the implementation of BS-VI emission standards, effective exhaust after-treatment has become critical in minimizing harmful emissions from diesel engines. One significant challenge is the accumulation of hydrocarbons (HC) in the Diesel Oxidation Catalyst (DOC). Certain hydrocarbons may adsorb onto the catalyst surface yet remain unreactive, leading to potential operational inefficiencies. This phenomenon necessitates the desorption of unreactive hydrocarbons to allow space for more reactive species, thereby enhancing oxidation efficiency and overall catalyst performance. The process of desorption (DeSorb) is vital to maintaining the balance of reactive hydrocarbons within the DOC. When a vehicle is idling, unburnt fuel produces hydrocarbons that accumulate in the DOC. Upon acceleration, these hydrocarbons can lead to an uncontrolled rise in temperature, resulting in DOC push-out, catalyst damage, and downstream impacts on the Diesel Particulate Filter (DPF). To mitigate these
The legislation of CEV Stage V emission norms has necessitated advanced Diesel Particulate Filter calibration strategies to ensure optimal performance across diverse construction equipment applications in the Indian market. Considering the various duty cycles of cranes, backhoe loaders, forklifts, compactors, graders, and other equipment, different load conditions and operational environments require a comprehensive strategy to enhance DPF efficiency, minimize regeneration frequency, and maintain compliance with emission standards. The DPF, as an after-treatment system in the exhaust layout, is essential for meeting emission standards, as it effectively traps particulate matter. Regeneration occurs periodically to burn the soot particles trapped inside the DPF through ECU management. Therefore, understanding soot loading and in-brick DPF temperature behavior across various applications is key. This paper explores the challenges in DPF calibration for CEV Stage V and provides a
This study explores the application of reverse engineering (RE) and digital twin (DT) technology in the design and optimization of advanced powertrain systems. Traditional approaches to powertrain development often rely on legacy designs with limited adaptability to modern efficiency and emission standards. In this work, we present a methodology combining 3D scanning, computational modeling, and machine learning to reconstruct, analyze, and enhance internal combustion engines (ICEs) and electric vehicle (EV) drivetrains. By digitizing physical components through RE, we generate high-fidelity DT models that enable virtual testing, performance prediction, and iterative improvement without costly physical prototyping. Key innovations include a novel mesh refinement technique for scanned geometries and a hybrid simulation framework integrating finite element analysis (FEA) and multi-body dynamics (MBD). Our case study demonstrates a 12% increase in thermal efficiency for a retrofitted ICE
After the implementation of BS-VI emission standards, effective exhaust after-treatment has become critical in minimizing harmful emissions from diesel engines. One significant challenge is the accumulation of hydrocarbons (HC) in the Diesel Oxidation Catalyst (DOC). Certain hydrocarbons may adsorb onto the catalyst surface yet remain unreactive, leading to potential operational inefficiencies. This phenomenon necessitates the desorption of unreactive hydrocarbons to allow space for more reactive species, thereby enhancing oxidation efficiency and overall catalyst performance. The process of desorption (DeSorb) is vital to maintaining the balance of reactive hydrocarbons within the DOC. When a vehicle is idling, unburnt fuel produces hydrocarbons that accumulate in the DOC. Upon acceleration, these hydrocarbons can lead to an uncontrolled rise in temperature, resulting in DOC push-out, catalyst damage, and downstream impacts on the Diesel Particulate Filter (DPF). To mitigate these
Emissions regulations, such as Euro VI, drives the Automotive industry to innovate continuously in Engine development. One significant challenge is the engine oil pumping from the crankcase into the combustion chamber, where it participates in combustion, which contributes to increased Particulate Numbers and fails to meet Euro VI emission compliance. This issue is most noticeable during engine idling and motoring conditions. During this time, a higher negative pressure difference develops between the intake manifold, which is acting above the combustion chamber and the engine crankcase. This pressure difference drives oil-laden blow-by aerosols past piston rings during the intake stroke and through the valve stem seals, allowing oil into the combustion chamber. The impact of the pressure difference between the intake manifold and crankcase was studied by varying the crankcase pressure through crankcase ventilation system. The results confirm that oil entry into the combustion chamber
Anticipated NOX emission standards will require that selective catalytic reduction (SCR) systems sustain exhaust temperatures of 200°C or higher for effective conversion performance. Maintaining these temperatures becomes challenging during low-load conditions such as idling, deceleration, and coasting, which lower exhaust heat and must be addressed in both regulatory test cycles and day-to-day operation. Cylinder deactivation (CDA) has proven effective in elevating exhaust temperatures while also reducing fuel consumption. This study investigates a flexible 6-cylinder CDA system capable of operating across any combination of fixed firing modes and dynamic skip-firing patterns, where cylinders transition between activation states nearly cycle-by-cycle. This operational flexibility extends the CDA usable range beyond prior implementations. Data was primarily collected from a test cell engine equipped with the dynamic CDA system, while a matching engine in a 2018 long-haul sleeper cab
In the power industry, high-power Diesel Generator (DG) sets often utilize high power V-engine cylinder configurations to enhance power output within a compact design, ensuring smoother operation and reduced vibration. In this V-engine configurations, the exhaust gas mass flow rate is significantly higher compared to inline engines of similar displacement, due to the greater number of cylinders operating in a compact space, which leads to a higher volume of exhaust gases expelled in a shorter duration. This necessitates the use of a dual Exhaust After Treatment System (EATS) to effectively manage NOx emissions. High-power gensets typically emit NOx levels around 9 g/kWh, presenting significant challenges for developers in adhering to stringent emission standards. To address these challenges and meet CPCB IV+ emission norms, we propose a dual urea dosing system integrated with a novel control strategy aimed at optimizing the treatment of exhaust gases. This paper introduces a dual
In-Use emission compliance regulations globally mandate that machines meet emission standards in the field, beyond dyno certification. For engine manufacturers, understanding emission compliance risks early is crucial for technology selection, calibration strategies, and validation routines. This study focuses on developing analytical and statistical methods for emission compliance risk assessment using Fleet Intelligence Data, which includes high-frequency telematics data from over 500K machines, reporting more than 1000 measures at 1Hz frequency. Traditional analytical methods are inadequate for handling such big data, necessitating advanced methods. We developed data pipelines to query measures from the Enterprise Data Lake (A Structured Data storage system), address big data challenges, and ensure data quality. Regulatory requirements were translated into software logic and applied to pre-processed data for emission compliance assessment. The resulting reports provide actionable
The current and upcoming Internal Combustion Engine (ICE) emission norms are very stringent. It is difficult to meet emission standards with just combustion optimization techniques. As a result, post-treatment is required for Engine-out emissions. Otherwise, these hazardous gases impact the ecosystem of living beings. Many technologies are implemented at the exhaust for reducing the emissions. Diesel Particulate Filter (DPF) is one such technique to achieve lower Particulate Matter (PM) and Particulate Number (PN) emission goals. In order to achieve such emission reduction, the DPF undergoes periodic cleaning called regeneration. During regeneration, the exhaust systems including DPF are maintained at elevated temperatures to achieve proper cleaning. When the vehicle is in regeneration, sudden braking or accelerator pedal release leads to engine Drop to Idle speeds (DTI), which sharply increases the temperature gradient inside the DPF which may result in physical damage like cracks
In Diesel engine exhaust after treatment system (ATS), Nitrogen Oxides (NOx) emissions control is achieved via Selective Catalytic Reduction (SCR) in which AdBlue or Diesel Exhaust Fluid (DEF) plays vital role. But AdBlue freezes below -11°C due to which in cold climate conditions system performance becomes critical as it affects efficiency as well as overall performance leading to safety and compliance with emission standards issue. So, it is essential to have a probabilistic thermal model which can predict the AdBlue temperature as per ambient temperature conditions. The present paper focuses on developing Bayesian Network (BN) based algorithm for AdBlue system by modelling probability of key factors influencing on its performance including AdBlue temperature, Ambient temperature, Coolant temperature, Coolant flow, Vehicle operating conditions etc. The BN Model predicts and ensures continuous learning and improvement of the system, based on operational data. Methodology proposed in
The effective reduction of particulate emissions from modern vehicles has shifted the focus toward emissions from tire wear, brake wear, road surface wear, and re-suspended particulate emissions. To meet future EU air quality standards and even stricter WHO targets for PM2.5, a reduction in non-exhaust particulate (NEP) emissions seems to be essential. For this reason, the EURO 7 emissions regulation contains limits for PM and PN emissions from brakes and tire abrasion. Graz University of Technology develops test methods, simulation tools and evaluates technologies for the reduction of brake wear particles and is involved in and leads several international research projects on this topic. The results are applied in emission models such as HBEFA (Handbook on Emission Factors). In this paper, we present our brake emission simulation approach, which calculates the power at the wheels and mechanical brakes, as well as corresponding rotational speeds for vehicles using longitudinal dynamics
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