Browse Topic: Fuel consumption
In response to increasing environmental awareness and the automotive industry's push for sustainability, the development of lightweight and robust components has become a key area of focus. This paper presents a multidisciplinary approach to the design and optimization of an aluminum parking brake lever, leveraging advanced structural optimization techniques to enhance performance while meeting stringent environmental standards. Traditional manufacturing processes for automotive components, such as stamping, often rely on steel due to its strength and ease of processing. However, the high density of steel can significantly impact the overall weight of the vehicle, leading to increased fuel consumption and emissions. In contrast, aluminum’s superior strength-to-weight ratio offers a promising alternative. This study employs Finite Element Analysis (FEA) to model the initial stress history of the lever, followed by the application of structural optimization tools to refine its geometry
Agrícola Cana Caiana and Grunner have developed an innovative vehicle for sugarcane harvesting, focused on reducing fuel consumption. This optimization is vital and relevant for similar operations in the largest global producers: Brazil (724 mi t - 37%), India (439 mi t - 22%), China (103 mi t - 5.3%), Thailand (92 mi t - 4.7%), Pakistan (88 mi t - 4.5%), Mexico (55 mi t - 2.8%), Colombia (35 mi t - 1.8%), Indonesia (32 mi t - 1.6%), USA (31 mi t - 1.6%), and Australia (28 mi t - 1.4%). In Brazil, São Paulo leads with 383.4 mi t (54.1% of the 23/24 harvest), followed by Minas Gerais (81.3 mi t). This innovative agricultural machinery, a result of the owners' experience, has already sold over a thousand units, proving its impact on the efficiency of the sugar-alcohol sector. The Belei family's expertise generated this solution that optimizes resources and increases harvesting productivity, with the potential to advance sustainability and profitability globally, driving agricultural
Flex-fueled vehicles (FFV) dominate the Brazilian market, accounting for over 75% of the national fleet. Ethanol fuel is widely used, primarily in the form of hydrated ethyl alcohol fuel (HEAF). Given the similar physicochemical properties of ethanol and methanol, fuel adulteration is a growing concern, often involving the addition of anhydrous ethanol, methanol, or even water to hydrated ethanol. These adulterants are visually imperceptible and can only be detected through analyses conducted by regulatory agencies using specialized instruments. However, they can significantly affect vehicle performance and accelerate engine component deterioration. The experiment was performed with a small displacement 3-cylinder port fuel injection flex-fuel engine on an engine test bench (dynamometer) and compared when fueled with ethanol and methanol. Data acquisition included combustion pressure, spark plug temperature, torque, air-fuel ratio, fuel flow, spark maps, and the overall effects of
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
Heavy-duty mining is a highly demanding sector within the trucking industry. Mining companies are allocated coal mine sites, and fleet operators are responsible for efficiently extracting ore within the given timeframe. To achieve this, companies deploy dumper trucks that operate in three shifts daily to transport payloads out of the site. Consequently, uptime is crucial, necessitating trucks with exceptionally robust powertrains. The profitability of mining operations hinges on the efficient utilization of these dumper trucks. Fuel consumption in these mines constitutes a significant portion of total expenses. Utilizing LNG as a fuel can help reduce operational fuel costs, thereby enhancing customer profitability. Additionally, employing LNG offers the potential to lower the CO2 footprint of mining operations. This paper outlines the creation of a data-driven duty cycle for mining vehicles and the simulation methodology used to accurately size LNG powertrain components, with a focus
The growing demand for improved fuel efficiency and reduced emissions in diesel engines has led to significant advancements in power management technologies. This paper presents a dual-mode functional strategy that integrates electrified turbochargers to enhance engine performance, provide boost and generate electrical power. This helps in optimizing the overall engine efficiency. The engine performance is enhanced with boosting mode where the electric motor accelerates the turbocharger independent of exhaust flow, effectively reducing turbo lag and provides immediate boost at low engine speeds. This feature also improves high altitude performance of the engine. Conversely, in generating mode, the electric turbocharger recovers or harvest energy from exhaust gases depending on engine operating conditions, converting it into electrical energy for battery recharging purpose. Advanced control systems enable real-time adjustments to boost pressure and airflow in response to dynamic driving
Evaluating the impact of software changes on fuel consumption and emissions is a critical aspect of transmission development. To evaluate the trade-offs between performance improvements and potential negative effects on efficiency, a forward-looking Software-in-the-Loop (SiL) simulation has been developed. Unlike backward calculations that derive fuel consumption based solely on cycle speed and engine speed, this approach executes complete driving cycles as the Worldwide Harmonized Light-Duty Vehicle Test Cycle (WLTC) within a detailed SiL environment. By considering all relevant influencing factors in a dynamic simulation, the method provides a more accurate assessment of fuel consumption and emission differences between two versions of the transmission software. The significant contribution of this work lies in the high-fidelity integration of a real virtual Transmission Control Unit (vTCU) software within a comprehensive, validated forward-looking SiL environment. This approach
The calibration of automotive electronic control units is a critical and resource-intensive task in modern powertrain development. Optimizing parameters such as transmission shift schedules for minimum fuel consumption traditionally requires extensive prototype testing by expert calibrators. This process is costly, time-consuming, and subject to variability in environmental conditions and human judgment. In this paper, an artificial calibrator is introduced – a software agent that autonomously tunes transmission shift maps using reinforcement learning (RL) in a Software-in-the-Loop (SiL) simulation environment. The RL-based calibrator explores shift schedule parameters and learns from fuel consumption feedback, thereby achieving objective and reproducible optimizations within the controlled SiL environment. Applied to a 7-speed dual-clutch transmission (DCT) model of a Mild Hybrid Electric Vehicle (MHEV), the approach yielded significant fuel efficiency improvements. In a case study on
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