Optimal control of a motor-integrated hybrid powertrain for a two-wheeled vehicle suitable for personal transportation

The present research aims to propose an optimized configuration of the motor integrated power-train with an optimal controller suitable for small power-train based two wheeler automobile which can increase the system level efficiency without affecting drivability. This work will be the foundation for realizing the system in a production ready vehicle for the two wheeler OEM TVS Motor Company in India. A detailed power-train model is developed (from first principles) for the scooter vehicle, which is powered by a 110 cc spark ignition (SI) engine and coupled with two types of transmission, a continuous variable transmission (CVT) and a 4-speed manual transmission (MT). Both models are capable of simulating torque and NOx emission output of the SI engine and dynamic response of the full power-train. The torque production and emission outputs of the model are compared with experimental results available from TVS Motor Company. The CVT gear ratio model is developed using an indirect method and an analytical model. Both types of powertrain models are applied to perform a simulated study of fuel consumption, NOx emission and drivability study for a particular vehicle platform. In the next stage of work, the mathematical model for a brush-less direct current machine (BLDC) with the drive system and Li-Ion battery are developed. The models are verified and calibrated with the experimental results from TVS Motor Company. The BLDC machine is integrated with both the CVT and MT powertrain models in parallel hybrid configurations and a drive cycle simulation is conducted for different static assist levels by the electrical machines. The initial test confirms the need of optimal sizing of the powertrain components as well as an optimal control system. The detailed model of the powertrain is converted to a control-oriented model which is suitable for optimal control. This is followed by multi-objective optimization of different components of the motor-integrated powertrain using a single function as well as Pareto-Optimal methods. The objective function for the multi-objective optimization is proposed to reduce the fuel consumption with battery charge sustainability with least impact on the increase of financial cost and weight of the vehicle. The optimization is conducted by a nested methodology that involves Particle Swarm Optimization and a Non-dominated sorting genetic algorithm where, concurrently, a global optimal control is developed corresponding to the multi-objective design. The global optimal controller is designed using dynamic programming. The research is concluded with an optimal controller developed using the hp-collocation method. The objective function of the dynamic programming method and hp-collocation method is proposed to reduce fuel consumption with battery charge sustainability.Open Acces

A novel genetic programming approach to the design of engine control systems for the voltage stabilisation 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 vehi- cle generator set. The generator set comprises a 3-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 doesn’t rely on computationally expensive structures, yet retains inherent disturabance 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

Biogeography-Based Optimization of a Variable Camshaft Timing System

Automotive system optimization problems are difficult to solve with traditional optimization techniques because the optimization problems are complex, and the simulations are computationally expensive. These two characteristics motivate the use of evolutionary algorithms and meta-modeling techniques respectively. In this work, we apply biogeography-based optimization (BBO) to radial basis function (RBF)-based lookup table controls of a variable camshaft timing system for fuel economy optimization. Also, we reduce computational search effort by finding an effective parameterization of the problem, optimizing the parameters of the BBO algorithm for the problem, and estimating the cost of a portion of the candidate solutions in BBO with design and analysis of computer experiments (DACE). We find that we can improve fuel economy by 1.7 compared to the original control parameters, and we find effective, problem-specific values for BBO population size and mutation rate. Finally, we find that we can use a small number of samples to construct DACE models, and we can use these models to estimate a significant portion of the BBO candidate solutions each generation to reduce computation effort and still obtain good BBO solution

Biogeography-Based Optimization of a Variable Camshaft Timing System

Automotive system optimization problems are difficult to solve with traditional optimization techniques because the optimization problems are complex, and the simulations are computationally expensive. These two characteristics motivate the use of evolutionary algorithms and meta-modeling techniques respectively. In this work, we apply biogeography-based optimization (BBO) to radial basis function (RBF)-based lookup table controls of a variable camshaft timing system for fuel economy optimization. Also, we reduce computational search effort by finding an effective parameterization of the problem, optimizing the parameters of the BBO algorithm for the problem, and estimating the cost of a portion of the candidate solutions in BBO with design and analysis of computer experiments (DACE). We find that we can improve fuel economy by 1.7 compared to the original control parameters, and we find effective, problem-specific values for BBO population size and mutation rate. Finally, we find that we can use a small number of samples to construct DACE models, and we can use these models to estimate a significant portion of the BBO candidate solutions each generation to reduce computation effort and still obtain good BBO solution

Analytical target cascading framework for engine calibration optimisation

YesThis paper presents the development and implementation of an Analytical Target Cascading (ATC) Multi-disciplinary Design Optimisation (MDO) framework for the steady state engine calibration optimisation problem. The case is made that the MDO / ATC offers a convenient framework for the engine calibration optimisation problem based on steady state engine test data collected at specified engine speed / load points, which is naturally structured on 2 hierarchical levels: the “Global” level, associated with performance over a drive cycle, and “Local” level, relating to engine operation at each speed / load point. The case study of a gasoline engine equipped with variable camshaft timing (VCT) was considered to study the application of the ATC framework to a calibration optimisation problem. The paper describes the analysis and mathematical formulation of the VCT calibration optimisation as an ATC framework, and its Matlab implementation with gradient based and evolutionary optimisation algorithms. The results and performance of the ATC are discussed comparatively with the conventional two-stage approach to steady state calibration optimisation. The main conclusion from this research is that ATC offers a powerful and efficient approach for engine calibration optimisation, delivering better solutions at both “Global” and “Local” levels. Further advantages of the ATC framework is that it is flexible and scalable to the complexity of the calibration problem, and enables calibrator preference to be incorporated a priori in the optimisation problem formulation, delivering important time saving for the overall calibration development process.The research work presented in this paper was funded by UK Technology Strategy Board (TSB) through the CREO (Carbon Reduction through Engine Optimisation) project

Optimization and control of a dual-loop EGR system in a modern diesel engine

Focusing on the author's research aspects, the intelligent optimization algorithm and advanced control methods of the diesel engine's air path have been proposed in this work. In addition, the simulation platform and the HIL test platform are established for research activities on engine optimization and control. In this thesis, it presents an intelligent transient calibration method using the chaos-enhanced accelerated particle swarm optimization (CAPSO) algorithm. It is a model-based optimization approach. The test results show that the proposed method could locate the global optimal results of the controller parameters within good speed under various working conditions. The engine dynamic response is improved and a measurable drop of engine fuel consumption is acquired. The model predictive control (MPC) is selected for the controllers of DLEGR and VGT in the air-path of a diesel engine. Two MPC-based controllers are developed in this work, they are categorized into linear MPC and nonlinear MPC. Compared with conventional PIO controller, the MPC-based controllers show better reference trajectory tracking performance. Besides, an improvement of the engine fuel economy is obtained. The HIL test indicates the two controllers could be implemented on the real engine

Computational intelligence techniques for HVAC systems: a review

Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO2 emissions. The drive to reduce energy use and associated greenhouse gas emissions from buildings has acted as a catalyst in the development of advanced computational methods for energy efficient design, management and control of buildings and systems. Heating, ventilation and air conditioning (HVAC) systems are the major source of energy consumption in buildings and an ideal candidate for substantial reductions in energy demand. Significant advances have been made in the past decades on the application of computational intelligence (CI) techniques for HVAC design, control, management, optimization, and fault detection and diagnosis. This article presents a comprehensive and critical review on the theory and applications of CI techniques for prediction, optimization, control and diagnosis of HVAC systems.The analysis of trends reveals the minimization of energy consumption was the key optimization objective in the reviewed research, closely followed by the optimization of thermal comfort, indoor air quality and occupant preferences. Hardcoded Matlab program was the most widely used simulation tool, followed by TRNSYS, EnergyPlus, DOE–2, HVACSim+ and ESP–r. Metaheuristic algorithms were the preferred CI method for solving HVAC related problems and in particular genetic algorithms were applied in most of the studies. Despite the low number of studies focussing on MAS, as compared to the other CI techniques, interest in the technique is increasing due to their ability of dividing and conquering an HVAC optimization problem with enhanced overall performance. The paper also identifies prospective future advancements and research directions

New trends in electrical vehicle powertrains

The electric vehicle and plug-in hybrid electric vehicle play a fundamental role in the forthcoming new paradigms of mobility and energy models. The electrification of the transport sector would lead to advantages in terms of energy efficiency and reduction of greenhouse gas emissions, but would also be a great opportunity for the introduction of renewable sources in the electricity sector. The chapters in this book show a diversity of current and new developments in the electrification of the transport sector seen from the electric vehicle point of view: first, the related technologies with design, control and supervision, second, the powertrain electric motor efficiency and reliability and, third, the deployment issues regarding renewable sources integration and charging facilities. This is precisely the purpose of this book, that is, to contribute to the literature about current research and development activities related to new trends in electric vehicle power trains.Peer ReviewedPostprint (author's final draft

Addressing real-time control problems in complex environments using dynamic multi-objective evolutionary approaches

The demand for increased automation of industrial processes generates control problems that are dynamic, multi-objective and noisy at the same time. The primary hypothesis underlying this research is that dynamic evolutionary methods could be used to address dynamic control problems where con icting control criteria are necessary. The aim of this research is to develop a framework for on-line optimisation of dynamic problems that is capable of a) representing problems in a quantitative way, b) identifying optimal solutions using multi-objective evolutionary algorithms, and c) automatically selecting an optimal solution among alternatives. A literature review identi es key problems in the area of dynamic multi-objective optimisation, discusses the on-line decision making aspect, analyses existing Multi- Objective Evolutionary Algorithms (MOEA) applications and identi es research gap. Dynamic evolutionary multi-objective search and on-line a posteriori decision maker are integrated into an evolutionary multi-objective controller that uses an internal process model to evaluate the tness of solutions. Using a benchmark multi-objective optimisation problem, the MOEA ability to track the moving optima is examined with di erent parameter values, namely, length of pre-execution, frequency of change, length of prediction interval and static mutation rate. A dynamic MOEA with restricted elitism is suggested for noisy environments.To address the on-line decision making aspect of the dynamic multi-objective optimisation, a novel method for constructing game trees for real-valued multiobjective problems is presented. A novel decision making algorithm based on game trees is proposed along with a baseline random decision maker. The proposed evolutionary multi-objective controller is systematically analysed using an inverted pendulum problem and its performance is compared to Proportional{ Integral{Derivative (PID) and nonlinear Model Predictive Control (MPC) approaches. Finally, the proposed control approach is integrated into a multi-agent framework for coordinated control of multiple entities and validated using a case study of a tra c scheduling problem.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Fuel-efficient driving strategies

This thesis is concerned with fuel-efficient driving strategies for vehicles driving on roads with varying topography, as well as estimation of road grade\ua0and vehicle mass for vehicles utilizing such strategies. A framework referred\ua0to as speed profile optimization (SPO), is introduced for reducing the fuel\ua0or energy consumption of single vehicles (equipped with either combustion\ua0or electric engines) and platoons of several vehicles. Using the SPO-based\ua0methods, average reductions of 11.5% in fuel consumption for single trucks,\ua07.5 to 12.6% energy savings in electric vehicles, and 15.8 to 17.4% average\ua0fuel consumption reductions for platoons of trucks were obtained. Moreover,\ua0SPO-based methods were shown to achieve higher savings compared to\ua0the commonly used methods for fuel-efficient driving. Furthermore, it was\ua0demonstrated that the simulations are sufficiently accurate to be transferred\ua0to real trucks. In the SPO-based methods, the optimized speed profiles were\ua0generated using a genetic algorithm for which it was demonstrated, in a\ua0discretized case, that it is able to produce speed profiles whose fuel consumption\ua0is within 2% of the theoretical optimum.A feedforward neural network (FFNN) approach, with a simple feedback\ua0mechanism, is introduced and evaluated in simulations, for simultaneous estimation of the road grade and vehicle mass. The FFNN provided road grade\ua0estimates with root mean square (RMS) error of around 0.10 to 0.14 degrees,\ua0as well as vehicle mass estimates with an average RMS error of 1%, relative\ua0to the actual value. The estimates obtained with the FFNN outperform road\ua0grade and mass estimates obtained with other approaches