Samveg Saxena

Extending Lean Operating Limit and Reducing Emissions of Methane Spark-Ignited Engines Using a Microwave-Assisted Spark Plug

A microwave-assisted spark plug was used to extend the lean operating limit (lean limit) and reduce emissions of an engine burning methane-air. In-cylinder pressure data were collected at normalized air-fuel ratios of λ=1.46, λ=1.51, λ=1.57, λ=1.68, and λ=1.75. For each λ, microwave energy (power supplied to the magnetron per engine cycle) was varied from 0 mJ (spark discharge alone) to 1600 mJ. At lean conditions, the results showed adding microwave energy to a standard spark plug discharge increased the number of complete combustion cycles, improving engine stability as compared to spark-only operation. Addition of microwave energy also increased the indicated thermal efficiency by 4% at λ=1.68. At λ=1.75, the spark discharge alone was unable to consistently ignite the air-fuel mixture, resulting in frequent misfires. Although microwave energy produced more consistent ignition than spark discharge alone at λ=1.75, 59% of the cycles only partially burned. Overall, the microwave-assisted spark plug increased engine performance under lean operating conditions (λ=1.68) but did not affect operation at conditions closer to stoichiometric.

Exploring Strategies for Reducing High Intake Temperature Requirements and Allowing Optimal Operational Conditions in a Biogas Fueled HCCI Engine for Power Generation

This paper evaluates strategies for reducing the intake temperature requirement for igniting biogas in HCCI engines. HCCI combustion is a promising technology for stationary power generation using renewable fuels in combustion engines. Combustion of biogas in HCCI engines allows high thermal efficiency similar to Diesel engines, with low net CO2 and low NOx emissions. However, in order to ensure the occurrence of autoignition in purely biogas fueled HCCI engines, a high inlet temperature is needed. This paper presents experimental and numerical results. First, experimental analysis on a 4 cylinder, 1.9 L Volkswagen TDI Diesel engine running with biogas in HCCI mode shows high gross indicated mean effective pressure (close to 8 bar), high gross indicated efficiency (close to 45%) and NOx emissions below the 2010 US limit (0.27g/kWh). Stable HCCI operation is experimentally demonstrated with a biogas composition of 60% CH4 and 40% CO2 on a volumetric basis, inlet pressures of 2–2.2 bar (absolute) and inlet temperatures of 200–210°C for equivalence ratios between 0.19–0.29. At lower equivalence ratios, slight changes in inlet pressure and temperature caused large changes in cycle-to-cycle variations while at higher equivalence ratios these same small pressure and temperature variations caused large changes to ringing intensity. Second, numerical simulations have been carried out to evaluate the effectiveness of high boost pressures and high compression ratios for reducing the inlet temperature requirements while attaining safe operation and high power output. The one zone model in Chemkin was used to evaluate the ignition timing and peak cylinder pressures with variations in temperatures at IVC from 373 to 473 K. In-cylinder temperature profiles between IVC and ignition were computed using Fluent 6.3 and fed into the multi-zone model in Chemkin to study combustion parameters. According to the numerical results, the use of both higher boost pressures and higher compression ratios permit lower inlet temperatures within the safe limits experimentally observed and allow higher power output. However, the range of inlet temperatures allowing safe and efficient operation using these strategies is very narrow, and precise inlet temperature control is needed to ensure the best results.Copyright

Distributed optimal charging of electric vehicles for demand response and load shaping

This paper proposes three novel distributed algorithms to optimally schedule Plug-in Electric Vehicle (PEV) charging. We first define the global optimization problem, where we seek to control large heterogeneous fleets of PEVs to flatten a net Load Curve. We demonstrate that the aggregated objective can be distributed, via a new consensus variable. This leads to a dual maximization problem that can be solved in an iterative and decentralized manner: at each iteration, PEVs solve their optimal problem, communicate their response to the aggregator, which then updates a price signal. We propose three distributed algorithms to compute the optimal solution, namely a gradient ascent and two incremental stochastic gradient methods. We prove their rate of convergence, their precision level and expose their characteristics in terms of communication and privacy. Finally, we use the Vehicle-To-Grid simulator (V2Gsim), and present a set of case studies, with an application to flattening the “Duck Curve” in California.

Key Factors Influencing Autonomous Vehicles’ Energy and Environmental Outcome

Autonomous vehicles (AVs)—vehicles that operate without real-time human input—are a potentially disruptive technology. If widely adopted, there is the potential for significant impacts on the energy and environmental characteristics of the transportation sector. This paper provides an outline of key drivers likely to influence the magnitude and direction of these impacts. We identify three broad categories: vehicle characteristics, transportation network, and consumer choice. Optimistically, AVs could facilitate unprecedented levels of efficiency and radically reduce transportation sector energy and environmental impacts; on the other hand, consumer choices could result in a net increase in energy consumption and environmental impacts. As the technology matures and approaches market penetration, improved models of AV usage, especially consumer preferences, will facilitate the development of policies that promote reductions in energy consumption.

Quantifying electric vehicle battery degradation from driving vs. V2G services

Concerns about electric vehicle (EV) battery degradation hinders the implementation of Vehicle-to-Grid(V2G) technology. In this paper, a methodology is proposed to quantify EV battery degradation from driving only vs. driving and V2G services, based on a semi-empirical lithium-ion battery degradation model. A detailed EV battery pack thermal model and EV powertrain model are utilized to capture the battery temperature and working parameters including current, internal resistance and state-of-charge (SOC). We use the proposed method to simulate the battery degradation of three EVs for ten years. Simulation results show that grid discharge at power rates typical for vehicle charging and discharging will have only minor effects on battery degradation in comparison to the degradation incurred from driving and calendar aging.

The Transportation Leapfrog: Using Smart Phones to Collect Driving Data and Model Fuel Economy in India

E RNEST O RLANDO L AWRENCE B ERKELEY N ATIONAL L ABORATORY The Transportation Leapfrog: Using Smart Phones to Collect Driving Data and Model Fuel Economy in India Anand Gopal, Laura Schewel, Samveg Saxena, Amol Phadke Environmental Energy Technologies Division May 2013 This work was supported by the Regulatory Assistance Project through the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.

Understanding Loss Mechanisms and Identifying Areas of Improvement for HCCI Engines Using Detailed Exergy Analysis

This paper presents a detailed exergy analysis of homogeneous charge compression ignition (HCCI) engines, including a crank-angle resolved breakdown of mixture exergy and exergy destruction. Exergy analysis is applied to a multi-zone HCCI simulation including detailed chemical kinetics. The HCCI simulation is validated against engine experiments for ethanol-fueled operation. The exergy analysis quantifies the relative importance of different loss mechanisms within HCCI engines over a range of engine operating conditions. Specifically, four loss mechanisms are studied for their relative impact on exergy losses, including 1) the irreversible combustion process (16.4–21.5%), 2) physical exergy lost to exhaust gases (12.0–18.7%), 3) heat losses (3.9–17.1%), and 4) chemical exergy lost to incomplete combustion (4.7–37.8%). The trends in each loss mechanism are studied in relation to changes in intake pressure, equivalence ratio, and engine speed as these parameters are directly used to vary engine power output. This exergy analysis methodology is proposed as a tool to inform research and design processes, particularly by identifying the relative importance of each loss mechanism in determining engine operating efficiency.Copyright