Edward Winward

Bio: Edward Winward is an academic researcher from Loughborough University. The author has contributed to research in topics: Diesel engine & Turbocharger. The author has an hindex of 9, co-authored 39 publications receiving 336 citations. Previous affiliations of Edward Winward include Caterpillar Inc..

Real-Time Energy Management for Diesel Heavy Duty Hybrid Electric Vehicles

Abstract: In this paper, a fuzzy-tuned equivalent consumption minimization strategy (F-ECMS) is proposed as an intelligent real-time energy management solution for a conceptual diesel engine-equipped heavy duty hybrid electric vehicle (HEV). In the HEV, two electric motors/generators are mounted on the turbocharger shaft and engine shaft, respectively, which can improve fuel efficiency by capturing and storing energy from both regenerative braking and otherwise wasted engine exhaust gas. The heavy duty HEV frequently involved in duty cycles characterized by start–stop events, especially in off-road applications, whose dynamics is analyzed in this paper. The on-line optimization problem is formulated as minimizing a cost function in terms of weighted fuel power and electric power. In the cost function, a cost factor is defined for both improving energy transmission efficiency and maintaining the battery energy balance. To deal with the nonexplicit relationship between HEV fuel economy, battery state of charge (SOC), and control variables, the cost factor is fuzzy tuned using expert knowledge and experience. In relation to the fuel economy, the air-fuel ratio is an important factor. An online search for capable optimal variable geometry turbocharger (VGT) vane opening and exhaust gas recirculation (EGR) valve opening is also necessary. Considering the exhaust emissions regulation in diesel engine control, the boundary values of VGT and EGR actuators are identified by offline design-of-experiment tests. An online rolling method is used to implement the multivariable optimization. The proposed method is validated via simulation under two transient driving cycles, with the fuel economy benefits of 4.43% and 6.44% over the nonhybrid mode, respectively. Compared with the telemetry equivalent consumption minimization strategy, the proposed F-ECMS shows better performance in the sustainability of battery SOC under driving conditions with the rapid dynamics often associated with off-road applications.

An Explicit Model Predictive Control Framework for Turbocharged Diesel Engines

Abstract: The turbocharged diesel engine is a typical multi-input multioutput system with strong couplings, actuator constraints, and fast dynamics. This paper addresses the exhaust emission regulation in turbocharged diesel engines using an explicit model predictive control (EMPC) approach, which allows tracking of the time-varying setpoint values generated by the supervisory level controller while satisfying the actuator constraints. The proposed EMPC framework consists of calibration, engine model identification, controller formulation, and state observer design. The proposed EMPC approach has a low computation requirement and is suitable for implementation in the engine control unit on board. The experimental results on a turbocharged Cat C6.6 diesel engine demonstrate that the EMPC controller significantly improves the tracking performance of the exhaust emission variables in comparison with the decoupled single-input single-output control methods.

Modelling of vehicle interior noise at reduced scale

Abstract: The present paper describes some recent results on the development of simplified reduced-scale models that can be used for experimental studies of vehicle interior noise. In many important cases such simplified structural models can be described analytically, thus providing a developer with the effective engineering tools for prediction and mitigation of vehicle interior noise, especially on a design stage. The general approach is illustrated by a 1:4-scale simplified model of a car developed at Loughborough University – 'QUASICAR' (QUArter –Scale Interior Cavity Acoustic Rig). The model consists of a curved steel plate that is simply supported by two rigid side walls made of massive wooden panels. The effect of road irregularities exciting vehicle structural vibrations is imitated by electromagnetic shakers applied to the bottom of the steel plate. Measurements of structural vibrations and of the acoustic pressure generated inside the model are compared with the results of theoretical predictions.

Adaptive Sliding-Mode Observer Design for a Selective Catalytic Reduction System of Ground-Vehicle Diesel Engines

Abstract: In this work, we investigate the adaptive sliding-mode observer design problem for the selective catalytic reduction (SCR) system in diesel-engine aftertreatment systems. First, an uncertain three-state model is obtained. Two kinds of uncertainties are considered: uncertainties with slow variations and uncertainties with fast variations. For the uncertain model, an adaptive sliding-mode observer is proposed. Then, the observer gain tuning method is developed based on the stability analysis of the estimation error system. The proposed observer design method is applied to an SCR system of a medium-duty diesel engine. Experimental results and comparisons are provided to illustrate the advantages of the designed observer according to the proposed algorithm. Compared with the open-loop SCR model and the Luenburger-like observer, the proposed adaptive sliding-mode observer can achieve better performance.

Fast Nonlinear Model Predictive Control on FPGA Using Particle Swarm Optimization

Abstract: Nonlinear model predictive control (NMPC) requires a repeated online solution of a nonlinear optimal control problem. The computation load remains the main challenge for the real-time practical application of the NMPC technique, particularly for fast systems. This paper presents a fast NMPC algorithm implemented on a field-programmable gate array (FPGA) that employs a particle swarm optimization (PSO) algorithm to handle nonlinear optimization. The FPGA is used to explore the possibilities of parallel architecture for the substantial acceleration of NMPC. PSO is employed to achieve real-time operation due to its naturally parallel capabilities. The proposed FPGA-based NMPC-PSO controller consists of a random-number generator, a fixed-point arithmetic, a PSO solver, and a universal asynchronous receiver/transmitter communication interface. Then, this controller is applied to an engine idle speed control problem and demonstrated with an FPGA-in-the-loop testbench. The experimental results indicate that the NMPC-on-FPGA-chip strategy has good computational performance and achieves satisfactory control performance.

Multi-timescale Parametric Electrical Battery Model for Use in Dynamic Electric Vehicle Simulations

Abstract: Simulation of electric vehicles (EVs) over driving schedules within a fully dynamic EV simulator requires battery models capable of accurately and quickly predicting state of charge (SOC), $I$ – $V$ characteristics, and dynamic behavior of various battery types. An electric battery model utilizing multiple time constants, to address ranges of seconds, minutes, and hours, is developed. The model parameters include open-circuit voltage, series resistance, and equivalent RC circuits, with nonlinear dependence on battery SOC. The SOC captures effects from discharge and charge rate, temperature, and battery cycling. Thermal modeling predicting real-time battery temperature is introduced. One focus of this paper is presenting a systematic and generic methodology for parameter extraction as well as obtaining SOC factors through reasonable test work when evaluating any given lithium-ion (Li-ion), nickel-metal hydride, or lead-acid battery cell. In particular, data sets for a Panasonic CGR18650 Li-ion battery cell are tabulated for direct use. The Li-ion battery model is programmed into a MATLAB/Simulink environment and used as a power source within an existing comprehensive dynamic vehicle simulator. Validation of the Simulink model is through a battery testing apparatus with a hardware-in-the-loop driving schedule that cycles real batteries. Results from simulations and measurements of Li-ion battery packs show that the proposed battery model behaves well and interacts appropriately with other subcomponents of the vehicle simulator.