Evolution, challenges and path forward for low temperature combustion engines

Low temperature combustion (LTC) is a recent engine technology that can reduce the oxides of nitrogen (NOx) and soot emissions simultaneously while maintaining higher thermal efficiency. The present review work investigates the performance, emission and combustion characteristics of LTC mode engines. Partially premixed LTC (PPLTC), homogeneous charge compression ignition (HCCI), premixed charge compression ignition (PCCI) and reactivity controlled compression ignition (RCCI) modes are researched under LTC mode. In recent decades, different engine strategies have been employed to reduce exhaust emissions and to enhance thermal efficiency. Exhaust gas recirculation, variable valve timing (VVT), advanced fuel injection technologies are adapted to achieve LTC mode in internal combustion (IC) engines to get improved outcomes. This review highlights the properties of fuels, fuel supply systems, valve actuation mechanisms, engine operating conditions and its effects on the engine characteristics. This review provides a perspective plan to the researchers for enhancing the performance, emission and combustion behavior of an engine by using LTC mode with lower NOx and soot emissions. Among LTC mode engines, RCCI mode engine operates well in 60% load, 60% premixed ratio, 35:1 air-fuel ratio and 56% brake thermal efficiency within the combustion phasing control.

A review on low temperature combustion engines: Performance, combustion and emission characteristics

1-Hexanol as a sustainable biofuel in DI diesel engines and its effect on combustion and emissions under the influence of injection timing and exhaust gas recirculation (EGR)

Performance enhancement of microwave assisted regeneration in a wall-flow diesel particulate filter based on field synergy theory

Effect analysis on pressure drop of the continuous regeneration-diesel particulate filter based on NO2 assisted regeneration

We study distributed Kalman filtering over the wireless sensor network, where each sensor node is required to locally estimate the state of a linear time-invariant discrete-time system, using its own observations and those transmitted from its neighbors in the presence of data packet drops. This is an optimal one-step prediction problem under the framework of distributed estimation, assuming the TCP-like protocol. We first present the stationary distributed Kalman filter (DKF) that minimizes the local average error variance in the steady state at each sensor node, based on the stabilizing solution to the corresponding modified algebraic Riccati equation (MARE). The existence of the stabilizing solution to the MARE is addressed by adopting the stability margin, which can be computed by solving a set of linear matrix inequalities. Then, the Kalman consensus filter (KCF), consisting of the stationary DKF and a consensus term of prior estimates, is studied. Finally, the performance of the stationary DKF and KCF is illustrated by a numerical example.

Distributed Kalman Filtering Over Wireless Sensor Networks in the Presence of Data Packet Drops

Energy efficiency impact of EGR on organizing clean combustion in diesel engines

We study the state consensus problem for linear shift-invariant discrete-time homogeneous multiagent systems (MASs) over time-varying graphs. A novel approach based on the small gain theorem is proposed to design the consensus control protocols for both neutrally stable and neutrally unstable MASs. It is shown that the state consensus can be achieved for neutrally stable MASs under the uniform observability condition; for neutrally unstable MASs, the state consensus entails more than the uniform observability. Two numerical examples are worked out to illustrate our consensus results.

State Consensus for Discrete-Time Multiagent Systems Over Time-Varying Graphs

Mode Switching Control for Diesel Low Temperature Combustion with Fast Feedback Algorithms

We study distributed Kalman filtering over the wireless sensor network, where each sensor node is required to locally estimate the state of a discrete-time linear time-invariant system, using its own observations and those transmitted from its neighbors in the presence of data packet drops. This is an optimal one-step prediction problem under the framework of distributed estimation, assuming the TCP protocol. We first study the stationary distributed Kalman filter (DKF) in the presence of packet drops. The optimal estimation gain is derived based on the stabilizing solution to the modified algebraic Riccati equation (MARE) associated with the DKF. The MARE admits the stabilizing solution, if the stability margin, which can be computed by solving a set of linear matrix inequalities, is greater than or equal to one. Then the Kalman consensus filter (KCF), consisting of the stationary DKF and a consensus term of prior estimates, is proposed, followed by the stability analysis. Finally the performance of stationary DKF and KCF is illustrated by a numerical example.

Xiang Chen

Papers

Distributed Kalman Filtering Over Wireless Sensor Networks in the Presence of Data Packet Drops

Energy efficiency impact of EGR on organizing clean combustion in diesel engines

State Consensus for Discrete-Time Multiagent Systems Over Time-Varying Graphs

Mode Switching Control for Diesel Low Temperature Combustion with Fast Feedback Algorithms

Distributed Kalman filtering over wireless sensor networks in the presence of data packet drops