Hybrid Optimal Theory and Predictive Control for Power Management in Hybrid Electric Vehicle

This paper presents a nonlinear-model based hybrid optimal control technique to compute a suboptimal power-split strategy for power/energy management in a parallel hybrid electric vehicle (PHEV). The power-split strategy is obtained as model predictive control solution to the power management control problem (PMCP) of the PHEV, i.e., to decide upon the power distribution among the internal combustion engine, an electric drive, and other subsystems. A hierarchical control structure of the hybrid vehicle, i.e., supervisory level and local or subsystem level is assumed in this study. The PMCP consists of a dynamical nonlinear model, and a performance index, both of which are formulated for power flows at the supervisory level. The model is described as a bi-modal switched system, consistent with the operating mode of the electric ED. The performance index prescribing the desired behavior penalizes vehicle tracking errors, fuel consumption, and frictional losses, as well as sustaining the battery state of charge (SOC). The power-split strategy is obtained by first creating the embedded optimal control problem (EOCP) from the original bi-modal switched system model with the performance index. Direct collocation is applied to transform the problem into a nonlinear programming problem. A nonlinear predictive control technique (NMPC) in conjunction with a sequential quadratic programming solver is used to compute suboptimal numerical solutions to the PMCP. Methods for approximating the numerical solution to the EOCP with trajectories of the original bi-modal PHEV are also presented in this paper. The usefulness of the approach is illustrated via simulation results on several case studies

Experimental Demonstration of Model Predictive Control in a Medium-Sized Commercial Building

This paper presents the implementation and experimental demonstration results of a practically effective and computationally efficient model predictive control (MPC) algorithm used to optimize the energy use of the heating, ventilation, and air-conditioning (HVAC) system in a multi-zone medium-sized commercial building. Advanced building control technologies are key enablers for intelligent operations of future buildings, however, adopting these technologies are quite difficult in practice mainly due to the cost-sensitive nature of the building industry. This paper presents the results of implementing optimization-based control algorithm and demonstrates the effectiveness of its energy-saving feature and improved thermal comfort along with lessons-learned. The performance of the implemented MPC algorithm was estimated relative to baseline days (heuristic-based control) with similar outdoor air temperature patterns during the cooling and shoulder seasons (September to November, 2013), and it was concluded that MPC reduced the total electrical energy consumption by more than 20% on average while improving thermal comfort in terms of temperature and maintaining similar zone CO2 levels

Model Predictive Control and Fault Detection and Diagnostics of a Building Heating, Ventilation, and Air Conditioning System

The paper presents Model Predictive Control (MPC) and Fault Detection and Diagnostics (FDD) technologies, their on-line implementation, and results from several demonstrations conducted for a large-size HVAC system. The two technologies are executed at the supervisory level in a hierarchical control architecture as extensions of a baseline Building Management System (BMS). The MPC algorithm generates optimal set points for the HVAC actuator loops which minimize energy consumption while meeting equipment operational constraints and occupant comfort constraints. The MPC algorithm is implemented using a new tool, the Berkeley Library for Optimization Modeling (BLOM), which generates automatically an efficient optimization formulation directly from a simulation model. The FDD algorithm detects and classifies in real-time potential faults of the HVAC actuators based on data from multiple sensors. The performance and limitations of FDD and MPC algorithms are illustrated and discussed based on measurement data recorded from multiple tests

INDIVIDUALIZED DRUG RESPONSE RELATED TO GENETIC VARIATIONS OF CYTOCHROME P450 ISOFORMS AND OTHER ENZYMES

Abstract Gene polymorphism and single nucleotide polymorphism (SNPs) called "snips" might play a pivotal role in the future of the clinical therapy. Although not much is known about the distribution of SNPs in population and about the gene polymorphism of the drugmetabolizing enzymes, their investigation could represent an important tool in order to determine the precise therapy for maximum efficacy. SNPs represent a DNA sequence variation of a single nucleotide, variation that could determine and alter the genome sequence. If just one nucleotide (A -adenine, C -citosine, G -guanine, T -thymine) is changed this will definitely bring a change in the DNA sequence. In order to improve public health, since the map of the genome was created, scientists thought about new SNPs maps, which will bring a new vision in diagnosis, biological markers, drug therapy, and human response to disease. This review brings some insights in the knowledge of gene polymorphism regarding their impact on drug therapy and disease. Rezumat Polimorfismul genelor şi polimorfismul unui singur nucleotid (SNPs) ar putea juca un rol central în viitorul terapiei. Deşi distribuţia SNPs în populaţie şi polimorfismul genelor enzimelor cu rol în metabolismul medicamentelor nu sunt foarte cunoscute, investigarea acestora ar putea reprezenta un instrument important pentru determinarea tratamentului adecvat pentru o maximă eficacitate. SNPs reprezintă variaţia unei secvenţe de ADN a unui singur nucleotid, variaţie care ar putea determina şi modifica secvenţa genomului. În cazul în care doar unul dintre nucleotide (A -adenină, C -citozină, Gguanină, T -timină) este schimbat aceasta va duce cu siguranţă la o schimbare în secvenţa ADN-ului. În scopul îmbunătăţirii sănătăţii publice, harta genomului fiind deja creată, oamenii de ştiinţă s-au gândit la hărţi noi, ale SNPs-urilor, care vor aduce o nouă viziune în privinţa diagnosticelor, markerilor biologici, tratamentului medicamentos, şi în general a reacţiilor omului la boli. Acest articol abordează unele aspecte legate de nivelul actual al cunoştiinţelor privind polimorfismul genelor şi a impactului lor asupra tratamentului medicamentos şi a bolilor în general

Context and driver dependent hybrid electrical vehicle operation

This paper studies the driver and context changes during the operation of a hybrid electric vehicle (HEV) and their influence on fuel consumption. Firstly, a context estimation model to recognize driving styles is developed based on machine learning techniques, for which a realistic scenario with simulation of urban mobility (SUMO) and car modeling platform (IPG Carmaker) integration is designed. Secondly, a novel context-aware control strategy based on model predictive control with extended prediction self-adaptive control (MPC-EPSAC) strategy is proposed. The control objective is to achieve optimal torque-split distribution, while optimizing fuel consumption in the parallel HEV. The simulation results suggest that an improvement in fuel economy can be achieved when the driving style in the control loop is adequately considered. Copyright (C) 2020 The Authors

Optimal Control of Nonlinear Switched Systems: Computational Methods and Applications

A switched system is a dynamic system that operates by switching between different subsystems or modes. Such systems exhibit both continuous and discrete characteristics—a dual nature that makes designing effective control policies a challenging task. The purpose of this paper is to review some of the latest computational techniques for generating optimal control laws for switched systems with nonlinear dynamics and continuous inequality constraints. We discuss computational strategiesfor optimizing both the times at which a switched system switches from one mode to another (the so-called switching times) and the sequence in which a switched system operates its various possible modes (the so-called switching sequence). These strategies involve novel combinations of the control parameterization method, the timescaling transformation, and bilevel programming and binary relaxation techniques. We conclude the paper by discussing a number of switched system optimal control models arising in practical applications

Automotive Powertrain Control — A Survey

This paper surveys recent and historical publications on automotive powertrain control. Control-oriented models of gasoline and diesel engines and their aftertreatment systems are reviewed, and challenging control problems for conventional engines, hybrid vehicles and fuel cell powertrains are discussed. Fundamentals are revisited and advancements are highlighted. A comprehensive list of references is provided.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/72023/1/j.1934-6093.2006.tb00275.x.pd

A TECHNICAL REPORT ON A POLYTOPIC SYSTEM APPROACH FOR THE HYBRID CONTROL OF A DIESEL ENGINE USING VGT/EGR

This paper develops a hybrid/gain scheduled control to move a diesel engine through a driving profile represented as a set of 12 operating points in the 7-dimensional state space of a 7th order nonlinear state model. The calculations for the control design are based on a 3rd order(reduced) model of the Diesel engine on which state space is projected the 12 operating points. About each operating point, we generate a 3rd order nonlinear error models of the Diesel engine. Using the error model for each operating point, a control design is set forth as a system of LMI\u27s. The solution of each system of LMI\u27s produces a norm bounded controller guaranteeing that x x i d i d - Æ 1 where xi d is the i-th desired operating point in the 3-dimensional state space. The control performance is then evaluated on the 7th order model