Robust oxygen fraction estimation for conventional and premixed charge compression ignition engines with variable valve actuation

In-cylinder oxygen fraction serves as a critical control input to advanced combustion strategies, but is extremely difficult to measure on production engines. Fortunately, the in-cylinder oxygen levels can be estimated based on accurate estimates or measurements of the oxygen fraction in the intake and exhaust manifolds, the in-cylinder charge mass, and the residual mass. This paper outlines such a physically based, generalizable strategy to estimate the in-cylinder oxygen fraction from only production viable measurements or estimates of exhaust oxygen fraction, fresh air flow, charge flow, fuel flow, turbine flow and EGR flow. While several of these flows are accurately measured or estimated, significant errors in the turbine and EGR flows are commonly observed and can highly degrade the accuracy of any calculations which utilize these flows. An EGR flow estimator was developed to improve the accuracy of this flow measurement over the stock engine control module (ECM) method and is detailed in this paper. Furthermore, the in-cylinder oxygen estimation algorithm is developed, and proven, to be robust to turbine flow errors. Regulation of in-cylinder oxygen levels is of interest for not only in conventional combustion modes but also in advanced combustion strategies such as premixed charge compression ignition. The proposed oxygen fraction estimator is designed such that its performance and stability is ensured in both conventional and advanced combustion modes. The model-based observer estimates the oxygen fractions to be within 0.5% O₂ and is shown to have exponential estimator error convergence with a time constant less than 0.05 s, even with turbine flow errors of up to 25%.     

Neural Network Controller Development and Implementation for Spark Ignition Engines With High EGR Levels

Past research has shown substantial reductions in the oxides of nitrogen (NO_x) concentrations by using 10% -25% exhaust gas recirculation (EGR) in spark ignition (SI) engines (see Dudek and Sain, 1989). However, under high EGR levels, the engine exhibits strong cyclic dispersion in heat release which may lead to instability and unsatisfactory performance preventing commercial engines to operate with high EGR levels. A neural network (NN)-based output feedback controller is developed to reduce cyclic variation in the heat release under high levels of EGR even when the engine dynamics are unknown by using fuel as the control input. A separate control loop was designed for controlling EGR levels. The stability analysis of the closed-loop system is given and the boundedness of the control input is demonstrated by relaxing separation principle, persistency of excitation condition, certainty equivalence principle, and linear in the unknown parameter assumptions. Online training is used for the adaptive NN and no offline training phase is needed. This online learning feature and model-free approach is used to demonstrate the applicability of the controller on a different engine with minimal effort. Simulation results demonstrate that the cyclic dispersion is reduced significantly using the proposed controller when implemented on an engine model that has been validated experimentally. For a single cylinder research engine fitted with a modern four-valve head (Ricardo engine), experimental results at 15% EGR indicate that cyclic dispersion was reduced 33% by the controller, an improvement of fuel efficiency by 2%, and a 90% drop in NO_x from stoichiometric operation without EGR was observed. Moreover, unburned hydrocarbons (uHC) drop by 6% due to NN control as compared to the uncontrolled scenario due to the drop in cyclic dispersion. Similar performance was observed with the controller on a different engine.     

Optimal heat release shaping in a reactivity controlled compression ignition (RCCI) engine

The present paper addresses the optimal heat release (HR) law in a single cylinder engine operated under reactivity controlled compression ignition (RCCI) combustion mode to minimise the indicated specific fuel consumption (ISFC) subject to different constraints including pressure related limits (maximum cylinder pressure and maximum cylinder pressure gradient). With this aim, a 0-dimensional (0D) engine combustion model has been identified with experimental data. Then, the optimal control problem of minimising the ISFC of the engine at different operating conditions of the engine operating map has been stated and analytically solved. To evaluate the method viability a data-driven model is developed to obtain the control actions (gasoline fraction) leading to the calculated optimal HR, more precisely to the optimal ratio between premixed and diffusive combustion. The experimental results obtained with such controls and the differences with the optimal HR are finally explained and discussed.     

Model predictive controller with average emissions constraints for diesel airpath

Diesel airpath controllers are required to deliver good tracking performance whilst satisfying operational constraints and physical limitations of the actuators. Due to explicit constraint handling capabilities, model predictive controllers (MPC) have been successfully deployed in diesel airpath applications. Previous MPC implementations have considered instantaneous constraints on engine-out emissions in order to meet legislated emissions regulations. However, the emissions standards are specified over a drive cycle, and hence, can be satisfied on average rather than just instantaneously, potentially allowing the controller to exploit the trade-off between emissions and fuel economy. In this work, an MPC is formulated to maximise the fuel efficiency whilst tracking boost pressure and exhaust gas recirculation (EGR) rate references, and in the face of uncertainties, adhering to the input, safety constraints and constraints on emissions averaged over some finite time period. The tracking performance and satisfaction of average emissions constraints using the proposed controller are demonstrated through an experimental study considering the new European drive cycle.     

柴油机共轨压力模糊自适应PID控制研究

柴油机高压共轨燃油系统中,共轨压力决定了燃油喷射压力,共轨压力随不同工况的调节能力及其压力的稳定性从根本上影响着系统性能。针对共轨压力控制,设计了模糊PID控制器,增加了积分分离与轨压预控制技术,给出了共轨压力的控制策略和实现方法。通过对PID参数的在线自适应整定,实现了在不同柴油机工况下对不同共轨压力变化的最佳控制。台架实验结果表明,共轨压力随柴油机转速与单次喷油量的增加应相应提高;当柴油机转速较高时,PID控制器应采用较大的控制参数;轨压预控制可有效地减少轨压波动和缩短轨压稳定时间;提出的控制策略和实现方法可把轨压控制偏差稳定在1.7%以下。     

Optimal control solution of the automotive emission-constrained minimum fuel problem

The automotive industry is confronted with the conflicting goals of improving fuel economy, reducing exhaust emissions, and maintaining vehicle driveability. Difficulties arise in the application of optimal control theory to this problem because transient models of the fuel, emission and driveability responses do not exist. To circumvent these difficulties, the mathematical models are replaced by a sophisticated experimental test setup. To demonstrate the applicability of the optimal control approach without a mathematical model, the problem of the hot-start optimization of fuel economy subject to emission constraints problem is solved. Operational considerations necessitate the direct incorporation of the control functions into the gradient-type solution algorithm. The solution of this problem demonstrates the feasibility of the experimental optimal control approach. The second problem involves the cold-start portion of the Federal Test Procedure (FTP). The transient influences of the engine and catalytic converter warmup are analyzed by the optimization procedure and are reflected in the optimized control functions. Finally, the hot-start optimization program is generalized to include an explicit surge-type driveability constraints on the controls. Comparison of the results of the hot-start problems reveals the trade-off between fuel economy and driveability.     

Diagnostic analysis of a small-scale incinerator by the Garson index

The formation of PCDD/Fs (dioxins/furans) due to incomplete combustion in solid waste incinerators has caused tremendous public concern. Consequently, more stringent standards for combustion and emission control have been implemented in order to mitigate the formation of these substances. This change in regulations will inevitably result in shutting down many small-scale incinerators because of the expense incurred in retrofitting such systems. Yet there is still an acute need for building small-scale incinerators for the purposes of disease control, environmental sanitation, and financial savings in rural areas and remote communities. For this reason, it is still worthwhile to pursue an optimal management strategy for small-scale incinerators. Through using the Garson index derived by a neural networks model, we can identify which operating factor is the one most influential to combustion status. Research findings clearly indicate that supplementing the auxiliary fuel via an on/off control unit is not an ideal method of maintaining a stable combustion evidenced by its relatively lower Garson index. Therefore the control of the auxiliary fuel system must be properly upgraded in order to improve its handling of the combustion unit. The results also show that the amount of waste in batch-charging and the lowest temperature of the primary chamber during the previous feeding are critical operating factors in this type of incinerator; controlling the charging amount per each feed around 30 kg is optimal for mitigating the variance of combustion status in the small-scale incinerator.     

并联新能源汽车最优效率实时控制

为了提高并联混合动力汽车驱动系统的实时效率,降低燃油消耗,本文提出一种基于效率最优的协调控制策略.根据不同驱动模式下电池的充放电状态,建立了充放电状态下驱动系统的等效燃油消耗模型,在分析电池效率和发动机效率的基础上,得到驱动系统效率的统一表达式,进而通过建立不同功率需求不同荷电状态下系统最优效率的功率分配系数图谱,设计了系统效率最优的协调控制策略,协调控制策略根据优化的功率分配系数在发动机和电机间进行力矩分配,协调控制策略可以离线计算并实时执行.两种工况循环下的仿真结果表明效率最优控制策略能有效地提高混合动力系统实时效率和燃油经济性.     

Closed-loop diesel engine combustion phasing control based on crankshaft torque measurements

Methods for closed-loop combustion phasing control in a diesel engine, based on measurements of crankshaft torque, are developed and evaluated. A model-based method for estimation of cylinder individual torque contributions from the crankshaft torque measurements is explained and a novel approach for identification of crankshaft dynamics is proposed. The use of the combustion net torque concept for combustion phasing estimation in the torque domain is also described. Two different control schemes, one for individual cylinder control and one for average cylinder control, are studied. The proposed methods are experimentally evaluated using a light-duty diesel engine equipped with a crankshaft integrated torque sensor. The results indicate that it is possible to estimate and control on a cylinder individual basis using the measurements from the crankshaft torque sensor. Combustion phasing is estimated with bias levels of less than 0.5 crank angle degrees (CAD) and cycle-to-cycle standard deviations of less than 0.7 CAD for all cylinders and the implemented combustion phasing controllers manage to accurately counteract disturbances in both fuel injection timing and EGR fraction.     

基于多目标遗传算法的柴电混合动力船舶功率分配优化

船舶在传统的柴油机推进模式时,低负荷下船用主机性能不佳,燃油消耗率高,燃烧质量差,在提高经济指标和减少排放指标方面遇到瓶颈。柴-电动混合动力推进形式能够通过合理的分配,有效降低燃料消耗和排放。针对混合动力船舶的动力结构,构建关于油耗和污染物排放的多目标优化模型。采用多目标遗传算法(NSGA-II)优化功率在主机和发电机间的分配问题,旨在所有目标之间进行折衷处理,尽量达到Pareto最优。选取某深水三用工作船的典型负荷进行优化计算和仿真验证,仿真结果表明优化后的结果能够兼顾排放性和经济性,提高船舶能效。     

Fuel consumption reduction effect of pre-acceleration before gliding of a vehicle with free-wheeling

Advanced fuel economy strategies are expected to reduce the fuel consumption of vehicles. An internal combustion engine (ICE) driving vehicle equipped with free-wheeling turns off the fuel injection and decouples the engine from the drivetrain when the driving force is not required. This paper proposes a method to reduce the fuel consumption of a vehicle equipped with free-wheeling. First, an optimization problem is formulated to minimize the fuel consumption of a vehicle with freewheeling when the traveling distance, the initial and final speed are specified and the vehicle needs to glide before arriving at the end point for fuel economy. The speed profile of the vehicle, engine operating point, and engine on/off timing are obtained as the results of the optimization. The analytical and numerical analyses results demonstrate the effectiveness and the fuel-saving mechanism of the obtained speed profile. The main finding of the analyses is that rather than starting a gliding stage immediately after an acceleration or a constant speed stage, adding a pre-acceleration stage before the gliding stage is more fuel-economic under some conditions independent of the complexity of the vehicle model. The obtained speed profile including a pre-acceleration stage is applied to a driving scenario including traffic congestions. The results demonstrate the effectiveness of the pre-acceleration stage in reducing fuel consumption for a vehicle equipped with free-wheeling.     

Fuel efficiency‐oriented platooning control of connected nonlinear vehicles: A distributed economic MPC approach

This paper considers the fuel efficiency‐oriented platooning control problem of connected vehicles. We present a novel distributed economic model predictive control (EMPC) approach to solve the problem of the vehicle platoon subject to nonlinear dynamics and safety constraints. In order to improve fuel economy of the whole vehicle platoon, the fuel consumption criterion is used to design the distributed EMPC strategy for the platoon. Meanwhile, the car‐tracking performance is exploited to guarantee stability and string stability of the platoon. Then the fuel efficiency control problem of the platoon is formulated as a distributed dual‐layer economic optimal control problem, which is solved in a fashion of receding horizon. It is proved that the proposed strategy guarantees asymptotic stability and predecessor‐follower string stability as well as fuel economy of the whole platoon by minimizing the fuel consumption cost. Finally, the effectiveness of the proposed strategy is highlighted by comparing its performance with that of the traditional distributed MPC strategy in numerical simulations.     

Nonlinear optimal tracking control with application to super-tankers for autopilot design

A new method is introduced to design optimal tracking controllers for a general class of nonlinear systems. A recently developed recursive approximation theory is applied to solve the nonlinear optimal tracking control problem explicitly by classical means. This reduces the nonlinear problem to a sequence of linear-quadratic and time-varying approximating problems which, under very mild conditions, globally converge in the limit to the nonlinear systems considered. The converged control input from the approximating sequence is then applied to the nonlinear system. The method is used to design an autopilot for the ESSO 190,000-dwt oil tanker. This multi-input-multi-output nonlinear super-tanker model is well established in the literature and represents a challenging problem for control design, where the design requirement is to follow a commanded maneuver at a desired speed. The performance index is selected so as to minimize: (a) the tracking error for a desired course heading, and (b) the rudder deflection angle to ensure that actuators operate within their operating limits. This will present a trade-off between accurate tracking and reduced actuator usage (fuel consumption) as they are both mutually dependent on each other. Simulations of the nonlinear super-tanker control model are conducted to illustrate the effectiveness of the nonlinear tracking controller.     

A model-based gain scheduling approach for controlling the common-rail system for GDI engines

The progressive reduction in vehicle emission requirements have forced the automotive industry to invest in research for developing alternative and more efficient control strategies. All control features and resources are permanently active in an electronic control unit (ECU), ensuring the best performance with respect to emissions, fuel economy, driveability and diagnostics, independently from engine working point. In this article, a considerable step forward has been achieved by the common-rail technology which has made possible to vary the injection pressure over the entire engine speed range. As a consequence, the injection of a fixed amount of fuel is more precise and multiple injections in a combustion cycle can be made. In this article, a novel gain scheduling pressure controller for gasoline direct injection (GDI) engine is designed to stabilise the mean fuel pressure into the rail and to track demanded pressure trajectories. By exploiting a simple control-oriented model describing the mean pressure dynamics in the rail, the control structure turns to be simple enough to be effectively implemented in commercial ECUs. Experimental results in a wide range of operating points confirm the effectiveness of the proposed control method to tame efficiently the mean value pressure dynamics of the plant showing a good accuracy and robustness with respect to unavoidable parameters uncertainties, unmodelled dynamics, and hidden coupling terms.     

Hybrid modelling of homogeneous charge compression ignition (HCCI) engine dynamics—a survey

The Homogeneous charge compression ignition (HCCI) principle holds promise to increase efficiency and to reduce emissions from internal combustion engines. As HCCI combustion lacks direct ignition timing control and auto-ignition depends on the operating condition, control of auto-ignition is necessary. Since auto-ignition of a homogeneous mixture is very sensitive to operating conditions, a fast combustion phasing control is necessary for reliable operation. To this purpose, HCCI modelling and model-based control with experimental validation were studied. A six-cylinder heavy-duty HCCI engine was controlled on a cycle-to-cycle basis in real time using a variety of sensors, actuators and control structures for control of the HCCI combustion. Combustion phasing control based on ion current was compared to feedback control based on cylinder pressure. With several actuators for controlling HCCI engines suggested, two actuators were compared, dual fuel and variable valve actuation (VVA). Model-based control synthesis requiring dynamic models of low complexity and HCCI combustion models were estimated by system identification and by physical modelling, the physical models aiming at describing the major thermodynamic and chemical interactions in the course of an engine stroke and their influence on combustion phasing. The models identified by system identification were used to design model-predictive control (MPC) with several desirable features and today applicable to relatively fast systems, the MPC control results being compared to PID control results. Both control of the combustion phasing and control of load-torque with simultaneous minimization of the fuel consumption and emissions, while satisfying the constraints on cylinder pressure, were included.     

Optimization and control of a stand-alone hybrid solid oxide fuel cells/gas turbine system coupled with dry reforming of methane

This paper presents the hybrid solid oxide fuel cells (SOFC)/gas turbine (GT) system coupled with dry reforming of methane (DRM). The DRM is a syngas producer by consuming greenhouse gas. The stand-alone (off-the-grid) power system is developed by using a combination of a post-burner, recuperators and pressurized recycles in place of external energy supplies. To address the stand-alone operation and meet the complete combustion condition for the burner, the optimal operating conditions are initially determined by solving a constrained optimization algorithm for maximizing the hybrid power efficiency, and the dynamic control loops are implemented in a plantwide environment. In the proposed plantwide control strategy, the inventory control framework is added to regulate the plant component inventory, an air/fuel cross-limiting combustion control is added to ensure complete combustion and reduce heat loss, and the power and CO₂ emission control configuration is added to achieve the quality control performance. Finally, the simulation shows that the IMC-based multi-loop control scheme can efficiently regulate the total system power and control CO₂ emissions per kWh of electricity as well.     

Quantitative feedback design of air and boost pressure control system for turbocharged diesel engines

For modern diesel engines, variable geometry turbocharger (VGT) is used to boost engine power output. In addition, exhaust gas recirculation (EGR) is utilized to reduce engine out NO_x emission. To realize these functions, a multivariable control system needs to control both VGT and EGR valve to deliver desired intake manifold (or boost) pressure, and desired EGR flow rate. This two-input and two-output system is nonlinear with cross-couplings between the boost and EGR responses to the input actuators, the system parameters are varying with different engine operating conditions. This paper proposes a closed loop design of a multivariable VGT/EGR control system for a turbocharged diesel engine. The control system is synthesized based on quantitative feedback theory to maintain robust stability and performance via sequential MIMO loop shaping in the frequency domain. Experiment results are included from a turbocharged diesel engine to show the effectiveness of the proposed control design.     

Optimal back-off point determination and controller weight selection for multivariate systems under finite-horizon control

Plant economic performance is most often related to the operating point, specifically the mean values of the process variables; meanwhile, most existing performance assessment techniques involve examining the variances or covariances of the controlled variables. A combined approach is to determine the appropriate trade-off between variances of different process variables in order to operate the plant at the point that provides maximum economic benefit while satisfying the operating constraints. This problem is referred to as the minimum backed-off operating point selection, and previous works have formulated it as a non-convex constrained optimization problem. In the current work, a new technique is introduced that can provide the optimal plant operating point. Additionally, this method provides the weights for a finite horizon controller that results in the optimal trade-off in process variable variances that will allow satisfaction of the operating constraints at the optimal operating point. In this method, the plant and disturbance models for the given process are used to generate data representing possible trade-offs between process variable standard deviations. Employing a piecewise linear regression to describe the sample points of this standard deviations data allows for the operating point selection problem to be solved as a small number of linear programs. The advantages of this approach are demonstrated through the use of mathematical and simulation case studies.     

Multimode combustion in a mild hybrid electric vehicle. Part 2: Three-way catalyst considerations

This is the second of a two-part study that discusses multimode combustion in a mild hybrid electric vehicle. Homogeneous charge compression ignition (HCCI) combustion oxidizes the oxygen storage capacity (OSC) of the three-way catalyst (TWC), thereby removing the TWC's ability to convert NOx under lean conditions. Despite prolonged operation in HCCI mode, enabled by the electric motor, the depletion of the OSC causes significant penalties in fuel economy and the amounts of tailpipe NOx are substantial. Counter-intuitively, it is seen that decreasing the sizes of both HCCI regime and OSC results in reduced tailpipe NOx while maintaining fuel economy benefits.     

Energy management strategies for parallel hybrid vehicles using fuzzy logic

This paper presents a fuzzy-logic-based energy management and power control strategy for parallel hybrid vehicles (PHV). The main objective is to optimize the fuel economy of the PHV, by optimizing the operational efficiency of all its components. The controller optimizes the power output of the electric motor/generator and the internal combustion engine by using vehicle speed, driver commands from accelerator and braking pedals, state of charge (SOC) of the battery, and the electric motor/generator speed. Separate controllers optimize braking and gear shifting. Simulation results show potential fuel economy improvement relative to other strategies that only maximize the efficiency of the combustion engine.