Optimal control of a flywheel-based automotive kinetic energy recovery system

This thesis addresses the control issues surrounding flywheel-based Kinetic Energy Recovery Systems (KERS) for use in automotive vehicle applications. Particular emphasis is placed on optimal control of a KERS using a Continuously Variable Transmission (CVT) for volume car production, and a wholly simulation-based approach is adopted. Following consideration of the general control issues surrounding KERS operation, a simplified system model is adopted, and the scope for use of optimal control theory is explored. Both Pontryagin’s Maximum Principle, and Dynamic Programming methods are examined, and the need for numerical implementation established. With Dynamic Programming seen as the most likely route to practical implementation for realistic nonlinear models, the thesis explores several new strategies for numerical implementation of Dynamic Programming, capable of being applied to KERS control of varying degrees of complexity. The best form of numerical implementation identified (in terms of accuracy and efficiency) is then used to establish via simulation, the benefits of optimal KERS control in comparison with a more conventional non-optimal strategy, showing clear benefits of using optimal control

Stochastic model predictive control for energy management of power-split plug-in hybrid electric vehicles based on reinforcement learning

In this paper, a stochastic model predictive control (MPC) method based on reinforcement learning is proposed for energy management of plug-in hybrid electric vehicles (PHEVs). Firstly, the power transfer of each component in a power-split PHEV is described in detail. Then an effective and convergent reinforcement learning controller is trained by the Q-learning algorithm according to the driving power distribution under multiple driving cycles. By constructing a multi-step Markov velocity prediction model, the reinforcement learning controller is embedded into the stochastic MPC controller to determine the optimal battery power in predicted time domain. Numerical simulation results verify that the proposed method achieves superior fuel economy that is close to that by stochastic dynamic programming method. In addition, the effective state of charge tracking in terms of different reference trajectories highlight that the proposed method is effective for online application requiring a fast calculation speed

An Optimal Control Model for the Diffusion of Innovation

The diffusion of technology has been modeled using different modeling techniques, and these models should fulfill different needs. For example, there are spatial models, models describing the technological side or the consumer side, and so on. On the other hand there are many models which just describe the process but few really give advice for necessary regulations or outside influence. Therefore, I have set up a model called DIFFOPT, which should describe the production aspect and the societal aspect of the diffusion process, and should also give explicit advice for investment and price policy of innovative technologies. This paper deals with the development of DIFFOPT which has the structure of an optimal control model, and with the mathematical description. Moreover the descriptive model which is basic for the optimal control model is tested computationally

Versatile modeling and optimization of fed batch processes for the production of secreted heterologous proteins with Pichia pastoris

BACKGROUND: Secretion of heterologous proteins depends both on biomass concentration and on the specific product secretion rate, which in turn is not constant at varying specific growth rates. As fed batch processes usually do not maintain a steady state throughout the feed phase, it is not trivial to model and optimize such a process by mathematical means. RESULTS: We have developed a model for product accumulation in fed batch based on iterative calculation in Microsoft Excel spreadsheets, and used the Solver software to optimize the time course of the media feed in order to maximize the volumetric productivity. The optimum feed phase consisted of an exponential feed at maximum specific growth rate, followed by a phase with linearly increasing feed rate and consequently steadily decreasing specific growth rate. The latter phase could be modeled also by exact mathematical treatment by the calculus of variations, yielding the explicit shape of the growth function, however, with certain indeterminate parameters. To evaluate the latter, one needs a numerical optimum search algorithm. The explicit shape of the growth function provides additional evidence that the Excel model results in correct data. Experimental evaluation in two independent fed batch cultures resulted in a good correlation to the optimized model data, and a 2.2 fold improvement of the volumetric productivity. CONCLUSION: The advantages of the procedure we describe here are the ease of use and the flexibility, applying software familiar to every scientist and engineer, and rapid calculation which makes predictions extremely easy, so that many options can be tested in silico quickly. Additional options like further biological and technological constraints or different functions for specific productivity and biomass yield can easily be integrated

Pontryagin's Minimum Principle based model predictive control of energy management for a plug-in hybrid electric bus

To improve computational efficiency of energy management strategies for plug-in hybrid electric vehicles (PHEVs), this paper proposes a stochastic model predictive controller (MPC) based on Pontryagin’s Minimum Principle (PMP), which differs from widely used dynamic programming (DP)-based predictive methods. First, short-time speed forecasting is achieved using a Markov chain model, based on real-world driving cycles. The PMP- and DP-based MPCs are compared under four preview horizons (5 s, 10 s, 15 s and 20 s), and the results show that the computational time of the DP-MPC is almost four times of that in the PMP-MPC. Moreover, the influence of predication horizon length on computational time and energy consumption is examined. Given a preview horizon of 5 s, the PMP-MPC holds a total energy consumption cost of 7.80 USD and computational time per second of 0.0130 s. When the preview horizon increases to 20 s, the total cost is 7.77 USD with the computational time per second increasing to 0.0502 s. Finally, DP, PMP, and rule-based strategies are contrasted to the PMP-MPC method, further demonstrating the promising performance and computational efficiency of the proposed methodology

A Novel Learning Based Model Predictive Control Strategy for Plug-in Hybrid Electric Vehicle

The multi-source electromechanical coupling renders energy management of plug-in hybrid electric vehicles (PHEVs) highly nonlinear and complex. Furthermore, the complicated nonlinear management process highly depends on knowledge of driving conditions, and hinders the control strategies efficiently applied instantaneously, leading to massive challenges in energy saving improvement of PHEVs. To address these issues, a novel learning based model predictive control (LMPC) strategy is developed for a serial-parallel PHEV with the reinforced optimal control effect in real time application. Rather than employing the velocity-prediction based MPC methods favored in the literature, an original reference-tracking based MPC solution is proposed with strong instant application capacity. To guarantee the optimal control effect, an online learning process is implemented in MPC via the Gaussian process (GP) model to address the uncertainties during state estimation. The tracking reference in LMPC based control problem in PHEV is achieved by a microscopic traffic flow analysis (MTFA) method. The simulation results validate that the proposed method can optimally manage energy flow within vehicle power sources in real time, highlighting its anticipated preferable performance

Genetic algorithm to optimization mobility-based dengue mathematical model

Implementation of vaccines, mosquito repellents and several Wolbachia schemes have been proposed recently as strategies against dengue. Research showed that the use of vaccine and repellent is highly effective when implemented to individuals who are in area with high transmission rates, while the use of Wolbachia bacteria is strongly effective when implemented in area with low transmission rates. This research is to show a three-strategy combination to cope with the dengue using mathematical model. In dengue mathematical model construction, several parameters are not yet known, therefore a genetic algorithm method was used to estimate dengue model parameters. Numerical simulation results showed that the combination of three strategies were able to reduce the number of infected humans. The dynamic of the human population with the combination of three strategies on average was able to reduce the infected human population by 45.2% in immobility aspect. Furthermore, the mobility aspect in dengue model was presented by reviewing two areas; Yogyakarta and Semarang in Indonesia. The numerical solutions showed that the trend graph was almost similar between the two areas. With the maximum effort given, the combination control values decreased slowly until the 100th day

A Gauss pseudospectral transcription for optimal control

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2005.Includes bibliographical references (p. 237-243).A pseudospectral method for solving nonlinear optimal control problems is proposed in this thesis. The method is a direct transcription that transcribes the continuous optimal control problem into a discrete nonlinear programming problem (NLP), which can be solved by well-developed algorithms. The method is based on using global polynomial approximations to the dynamic equations at a set of Gauss collocation points. The optimality conditions of the NLP have been found to be equivalent to the discretized optimality conditions of the continuous control problem, which is not true of other pseudospectral methods. This result indicates that the method can take advantage of the properties of both direct and indirect formulations, and allows for the costates to be estimated directly from the Lagrange multipliers of the NLP. The method has been shown empirically to have very fast convergence (exponential) in the states, controls, and costates, for problems with analytic solutions. This convergence rate of the proposed method is significantly faster than traditional finite difference methods, and has been demonstrated with many example problems. The initial costate estimate from the proposed method can be used to define an optimal feedback law for real time optimal control of nonlinear problems. The application and effectiveness of this approach has been demonstrated with the simulated trajectory optimization of a launch vehicle.by David Benson.Ph.D

Comment on "Nash and Stackelberg solutions in a differential game model of capitalism"

Game Theory;Capitalism