D. T. Gladwin

Analysis and Design of LLC Resonant Converters With Capacitor–Diode Clamp Current Limiting

This paper presents a design methodology for LLC resonant converters with capacitor-diode clamp for current limiting in overload conditions. A new fundamental harmonic approximation-based equivalent circuit model is obtained through the application of describing function techniques, by examining the fundamental behavior of the capacitor-diode clamp. An iterative procedure to determine the conduction point of the diode clamp is also given. The behavior of this type of converter is analyzed and guidelines for designing the current limiting characteristics are discussed. The characterization of a 90 W converter design using the proposed methodology is presented. The converter voltage gain and the voltage-current characteristics under different overload conditions and operating frequencies are predicted using the proposed model, which accuracies are validated against the prototype with good correlation.

Multiobjective analysis for the design and control of an electromagnetic valve actuator

Abstract The electromagnetic valve actuator can deliver much improved fuel efficiency and reduced emissions in spark ignition (SI) engines owing to the potential for variable valve timing when compared with cam-operated, or conventional, variable valve strategies. The possibility exists to reduce pumping losses by throttle-free operation, along with closed-valve engine braking. However, further development is required to make the technology suitable for acceptance into the mass production market. This paper investigates the application of multiobjective optimization techniques to the conflicting objective functions inherent in the operation of such a device. The techniques are utilized to derive the optimal force-displacement characteristic for the solenoid actuator, along with its controllability and dynamic/steady state performance.

Improved Bandwidth and Noise Resilience in Thermal Impedance Spectroscopy by Mixing PRBS Signals

This paper presents a method of mixing pseudo-random binary sequences (PRBSs) to form a new signal that can be used to obtain the thermal impedance spectrum of power electronic systems. The proposed technique increases the useful frequency range of a PRBS by mixing two identical sequences at different frequencies. The new signal incorporates the frequency responses of both contributions. Mixing can be performed using a number of mathematical operators and analysis reveals that AND is the operator of choice since it has the lowest average input power for the same effectiveness. The bandwidth, frequency-domain representation, and noise resilience of PRBS signals are also reported. It is shown that the noise floor is significantly reduced under the mixed technique, which allows lower impedances to be measured under noisy measurement conditions. For a typical 8-bit PRBS, mixing reduces the noise floor by a factor of 10.5. Simulated and experimental validation are performed and results show the mixed scheme offers increased bandwidth, reduced computation and improved noise resilience compared to single PRBS techniques.

Multi-objective evolutionary–fuzzy augmented flight control for an F16 aircraft

Abstract In this article, the multi-objective design of a fuzzy logic augmented flight controller for a high performance fighter jet (the Lockheed-Martin F16) is described. A fuzzy logic controller is designed and its membership functions tuned by genetic algorithms in order to design a roll, pitch, and yaw flight controller with enhanced manoeuverability which still retains safety critical operation when combined with a standard inner-loop stabilizing controller. The controller is assessed in terms of pilot effort and thus reduction of pilot fatigue. The controller is incorporated into a six degree of freedom motion base real-time flight simulator, and flight tested by a qualified pilot instructor.

A controlled migration genetic algorithm operator for hardware-in-the-loop experimentation

In this paper, we describe the development of an extended migration operator, which combats the negative effects of noise on the effective search capabilities of genetic algorithms. The research is motivated by the need to minimise the number of evaluations during hardware-in-the-loop experimentation, which can carry a significant cost penalty in terms of time or financial expense. The authors build on previous research, where convergence for search methods such as simulated annealing and variable neighbourhood search was accelerated by the implementation of an adaptive decision support operator. This methodology was found to be effective in searching noisy data surfaces. Providing that noise is not too significant, genetic algorithms can prove even more effective guiding experimentation. It will be shown that with the introduction of a controlled migration operator into the GA heuristic, data, which represents a significant signal-to-noise ratio, can be searched with significant beneficial effects on the efficiency of hardware-in-the-loop experimentation, without a priori parameter tuning. The method is tested on an engine-in-the-loop experimental example, and shown to bring significant performance benefits.

Viability of “second-life” use of electric and hybridelectric vehicle battery packs

In order for successful second-life implementation of Electric Vehicle (EV) battery packs, the viability of the intended second-life use must be ascertained based on a cost-benefit analysis and technical appraisal of the estimated condition of the available battery packs. This paper discusses the issues in measuring State-of-Health (SoH) and other battery condition metrics of a battery pack. Measurements on real-life battery packs sent for recycling are taken that demonstrate a typical 85% SoH; slightly higher than predicted by Original Equipment Manufacturers (OEM). A model is introduced that can simulate the energy demand in a home/dwelling being met by a number of sources including mains (utility) power, photovoltaic generation (PV), and second-life battery storage. The model is applied to three scenarios using second-life battery storage, to create energy costs savings through time-shifting of energy using on-peak/off-peak electricity tariffs. For each scenario a cost-benefit analysis is produced, indicating that whilst energy costs savings can be achieved, excessive usage of the battery pack can cause the payback period of the capital investment to be longer than the predicted second-lifetime of the battery pack. However, the final scenario demonstrates that combining the battery pack with local generation, such as PV, yields cost savings that are significant at 75%, and the payback period is within the estimated lifetime of the battery pack.

Internal combustion engine control for series hybrid electric vehicles by parallel and distributed genetic programming/multiobjective genetic algorithms

This article addresses the problem of maintaining a stable rectified DC output from the three-phase AC generator in a series-hybrid vehicle powertrain. The series-hybrid prime power source generally comprises an internal combustion (IC) engine driving a three-phase permanent magnet generator whose output is rectified to DC. A recent development has been to control the engine/generator combination by an electronically actuated throttle. This system can be represented as a nonlinear system with significant time delay. Previously, voltage control of the generator output has been achieved by model predictive methods such as the Smith Predictor. These methods rely on the incorporation of an accurate system model and time delay into the control algorithm, with a consequent increase in computational complexity in the real-time controller, and as a necessity relies to some extent on the accuracy of the models. Two complementary performance objectives exist for the control system. Firstly, to maintain the IC engine at its optimal operating point, and secondly, to supply a stable DC supply to the traction drive inverters. Achievement of these goals minimises the transient energy storage requirements at the DC link, with a consequent reduction in both weight and cost. These objectives imply constant velocity operation of the IC engine under external load disturbances and changes in both operating conditions and vehicle speed set-points. In order to achieve these objectives, and reduce the complexity of implementation, in this article a controller is designed by the use of Genetic Programming methods in the Simulink modelling environment, with the aim of obtaining a relatively simple controller for the time-delay system which does not rely on the implementation of real time system models or time delay approximations in the controller. A methodology is presented to utilise the miriad of existing control blocks in the Simulink libraries to automatically evolve optimal control structures.

Improved decision support for engine-in-the-loop experimental design optimization

Abstract Experimental optimization with hardware in the loop is a common procedure in engineering and has been the subject of intense development, particularly when it is applied to relatively complex combinatorial systems that are not completely understood, or where accurate modelling is not possible owing to the dimensions of the search space. A common source of difficulty arises because of the level of noise associated with experimental measurements, a combination of limited instrument precision, and extraneous factors. When a series of experiments is conducted to search for a combination of input parameters that results in a minimum or maximum response, under the imposition of noise, the underlying shape of the function being optimized can become very difficult to discern or even lost. A common methodology to support experimental search for optimal or suboptimal values is to use one of the many gradient descent methods. However, even sophisticated and proven methodologies, such as simulated annealing, can be significantly challenged in the presence of noise, since approximating the gradient at any point becomes highly unreliable. Often, experiments are accepted as a result of random noise which should be rejected, and vice versa. This is also true for other sampling techniques, including tabu and evolutionary algorithms. After the general introduction, this paper is divided into two main sections (sections 2 and 3), which are followed by the conclusion. Section 2 introduces a decision support methodology based upon response surfaces, which supplements experimental management based on a variable neighbourhood search and is shown to be highly effective in directing experiments in the presence of a significant signal-to-noise ratio and complex combinatorial functions. The methodology is developed on a three-dimensional surface with multiple local minima, a large basin of attraction, and a high signal-to-noise ratio. In section 2, the methodology is applied to an automotive combinatorial search in the laboratory, on a real-time engine-in-the-loop application. In this application, it is desired to find the maximum power output of an experimental single-cylinder spark ignition engine operating under a quasi-constant-volume operating regime. Under this regime, the piston is slowed at top dead centre to achieve combustion in close to constant volume conditions. As part of the further development of the engine to incorporate a linear generator to investigate free-piston operation, it is necessary to perform a series of experiments with combinatorial parameters. The objective is to identify the maximum power point in the least number of experiments in order to minimize costs. This test programme provides peak power data in order to achieve optimal electrical machine design. The decision support methodology is combined with standard optimization and search methods — namely gradient descent and simulated annealing— in order to study the reductions possible in experimental iterations. It is shown that the decision support methodology significantly reduces the number of experiments necessary to find the maximum power solution and thus offers a potentially significant cost saving to hardware-in-the-loop experimentation.

Generator voltage stabilisation for series-hybrid electric vehicles.

This paper presents a controller for use in speed control of an internal combustion engine for series-hybrid electric vehicle applications. Particular reference is made to the stability of the rectified DC link voltage under load disturbance. In the system under consideration, the primary power source is a four-cylinder normally aspirated gasoline internal combustion engine, which is mechanically coupled to a three-phase permanent magnet AC generator. The generated AC voltage is subsequently rectified to supply a lead-acid battery, and permanent magnet traction motors via three-phase full bridge power electronic inverters. Two complementary performance objectives exist. Firstly to maintain the internal combustion engine at its optimal operating point, and secondly to supply a stable 42 V supply to the traction drive inverters. Achievement of these goals minimises the transient energy storage requirements at the DC link, with a consequent reduction in both weight and cost. These objectives imply constant velocity operation of the internal combustion engine under external load disturbances and changes in both operating conditions and vehicle speed set-points. An electronically operated throttle allows closed loop engine velocity control. System time delays and nonlinearities render closed loop control design extremely problematic. A model-based controller is designed and shown to be effective in controlling the DC link voltage, resulting in the well-conditioned operation of the hybrid vehicle.

A novel genetic programming approach to the design of engine control systems for the voltage stabilization of hybrid electric vehicle generator outputs

This paper describes a Genetic Programming based automatic design methodology applied to the maintenance of a stable generated electrical output from a series-hybrid vehicle generator set. The generator set comprises a three-phase AC generator whose output is subsequently rectified to DC. The engine/generator combination receives its control input via an electronically actuated throttle, whose control integration is made more complex due to the significant system time delay. This time delay problem is usually addressed by model predictive design methods, which add computational complexity and rely as a necessity on accurate system and delay models. In order to eliminate this reliance, and achieve stable operation with disturbance rejection, a controller is designed via a Genetic Programming framework implemented directly in Matlab and, particularly, Simulink. The principal objective is to obtain a relatively simple controller for the time-delay system which does not rely on computationally expensive structures, yet retains inherent disturbance rejection properties. A methodology is presented to automatically design control systems directly upon the block libraries available in Simulink to automatically evolve robust control structures.