Course Syllabus J: Introduction to Hybrid and Electric Vehicles Fall semester, 2016

Transcription

1 Course Syllabus J: Introduction to Hybrid and Electric Vehicles Fall semester, 2016 Instructor: Orkun Karabasoglu, PhD Office Location: JIE 311B, JIE Office Hours: Monday 3pm Wechat ID: teslanewton Wechat course group: J Teaching Assistant(s): Hadi Amini Office Hours: Monday and Friday 2pm- 3pm. (Office space in front of JIE 311b Academic Services Assistant: Ziyan He Address: Office Location: Rm 515, JIE, East Campus Course Description: This course is an introduction to hybrid and plug-in electric vehicles. Equipped with a motor, battery, and gasoline engine, plug-in electric vehicles offer a viable solution to reduce gasoline consumption, emissions and fuel costs since they operate partly or entirely on inexpensive electricity that can be obtained from local, renewable, and green energy sources. In this course, student will learn the fundamentals of electrified vehicle design, control and optimization. Course will cover the topics such as vehicle component modeling, powertrain architectures, hybridization methods, energy management algorithms (rule based control, dynamic programming, and predictive control), battery state of charge estimation, battery cell balancing, powertrain performance analysis, optimal component sizing algorithms (DIRECT, GA, and PSO), hardware-in-the-loop-simulation, driver-in-the-loop simulation, EV-smart grid integration, vehicular networks (CAN bus and OBD-2 scanners), vehicle routing algorithms (Dijkstra and A*) and etc. Students will learn how to use PSAT/Autonomie software as well as Matlab/Simulink to design and analyze vehicle powertrains. Students will also learn how to design battery tests and run them using battery test stations and environmental chambers which are available in laboratory. Students equipped with the skills and knowledge that they will gain in this course will be highly sought-after in automotive industry in US and China. There will be biweekly assignments and a poster session at the end of the course where students will demonstrate their projects. Number of Units: 12

2 Pre-requisites: No requirement. However previously taken courses on control, optimization, machine learning, and power systems might be valuable as well as some experience in Matlab/Simulink. Research Area: Electric Vehicles, Automotive, Optimization, Control, Design Class Schedule: Lecture: Monday pm, 214 Friday pm, 214 Labs/Recitation: Odd weeks, Tuesday 10.30am, 214 Required Textbook: Introduction to Hybrid Vehicle System Modeling and Control by Wei Liu Control/dp/ /ref=pd_cp_b_1 >> Other Supplemental Materials: Lecturer will provide handouts for some some of the lectures. Brief List of Topics Covered: Course Blackboard: To access the course blackboard from an Andrew Machine, go to the login page at: You should check the course blackboard daily for announcements and handouts. Course Wiki: Students are encouraged to use the ECE wiki to provide feedback about the course at: Grading Algorithm: 30% Homework 15% Midterm 1 30% Project 25% Final 2

3 Tentative Course Calendar Date Day Class Activity August 31 Mon Introduction, course objectives, overview of hybrid and electric vehicle engineering, visit to lab (LIVES), introduction to equipment September 4 Fri Vehicle system analysis, power flow in hybrid vehicles, hybridization, and vehicle powertrain architectures 11 Fri Components of vehicles, gasoline engine, electric motor, energy storage, and transmission 14 Mon Powertrain system analysis toolkit, synthesizing and analyzing vehicle powertrains 18 Fri Modeling of hybrid vehicles, vehicle design criteria, and analysis of component selection 21 Mon Split powertrain and alternative control modes, discussion of Toyota Prius System 25 Fri Engine and electric motor modeling and performance analysis 28 Mon Energy storage system modeling and performance analysis October 2 Fri Transmission, final drive, wheel, vehicle body, PID based driver modeling 5 Mon EV energy management 1: Fundamental concepts 9 Fri EV energy management 2: Rule based control, Deterministic Dynamic Programming 12 Mon EV energy management 3: Predictive control of hybrid vehicles 16 Fri Hybrid vehicle optimal design and performance analysis, drive cycles, stochastic drive cycle modeling 19 Mon HEV component sizing algorithms 1: DIRECT, Genetic Algorithms 23 Fri HEV component sizing algorithms 2: Particle Swarm Optimization, Chaotic Fruit Fly Algorithm 26 Mon Vehicle combined design and control optimization, alternative co-design schemes 30 Fri Energy storage systems, measurements, SOC estimation (1), coulomb counting, November 2 Mon State of charge estimation (2), Kalman filters and Unscented transform 6 Fri Battery degradation models and life estimation, battery cell balancing, active and passive approaches 9 Mon Vehicle routing algorithms 1: transportation network modeling, intelligent driver model, energy consumption metamodels, Dijkstra algorithm 13 Fri Vehicle routing algorithms 2: A* algorithm, alternative objective functions, application and analysis 16 Mon Vehicular networks, CAN bus system, and OBD-2 scanners for vehicle data collection 20 Fri Hardware-in-the-loop simulation: Battery cell testing/control (Lab session) 23 Mon Driver-in-the-loop simulation: Virtual vehicle simulator and real time data collection and control (Lab session) 30 Mon NO CLASS SPRING CARNIVAL December 4 Fri EVs and power system integration 7 Mon Project Presentations 11 Fri Project Presentations Education Objectives (Relationship of Course to Program Outcomes) (a) an ability to apply knowledge of mathematics, science, and engineering on vehicle engineering. (b) an ability to design and conduct experiments, as well as to analyze and

4 interpret data using Matlab and Autonomie for the solution of problems related to vehicle engineering. (c) an ability to design a system, component, or process to meet desired needs within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability, and sustainability while doing the weekly assignments and final project related to hybrid vehicles design, analysis and control. (d) an ability to function on multi-disciplinary teams by working together as a team to complete the final project. (e) an ability to identify, formulate, and solve engineering problems related to vehicle engineering. (f) an ability to communicate effectively by project presentations, discussions with the advisor and other team members. (g) the broad education necessary to understand the impact of engineering solutions in a global, economic, environmental, and societal context while learning the impact of different vehicle powertrain technologies. (h) an ability to use the techniques, skills, and modern engineering tools necessary for vehicle engineering practice in industry and academia. Academic Integrity Policy ( The Department of Electrical and Computer Engineering adheres to the academic integrity policies set forth by Carnegie Mellon University and by the College of Engineering. ECE students should review fully and carefully Carnegie Mellon University's policies regarding Cheating and Plagiarism; Undergraduate Academic Discipline; and Graduate Academic Discipline. ECE graduate student should further review the Penalties for Graduate Student Academic Integrity Violations in CIT outlined in the CIT Policy on Graduate Student Academic Integrity Violations. In addition to the above university and college-level policies, it is ECE's policy that an ECE graduate student may not drop a course in which a disciplinary action is assessed or pending without the course instructor's explicit approval. Further, an ECE course instructor may set his/her own course-specific academic integrity policies that do not conflict with university and college-level policies; course-specific policies should be made available to the students in writing in the first week of class. This policy applies, in all respects, to this course. Carnegie Mellon University's Policy on Academic Integrity ( states the following, 4

5 Students at Carnegie Mellon are engaged in intellectual activity consistent with the highest standards of the academy. The relationship between students and instructors and their shared commitment to overarching standards of respect, honor and transparency determine the integrity of our community of scholars. The actions of our students, faculty and staff are a representation of our university community and of the professional and personal communities that we lead. Therefore, a deep and abiding commitment to academic integrity is fundamental to a Carnegie Mellon education. Honesty and good faith, clarity in the communication of core values, professional conduct of work, mutual trust and respect, and fairness and exemplary behavior represent the expectations for ethical behavior for all members of the Carnegie Mellon community. In any manner of presentation, it is the responsibility of each student to produce her/his own original academic work. Collaboration or assistance on academic work to be graded is not permitted unless explicitly authorized by the course instructor(s). Students may utilize the assistance provided by Academic Development, the Global Communication Center, and the Academic Resource Center (CMU-Q) unless specifically prohibited by the course instructor(s). Any other sources of collaboration or assistance must be specifically authorized by the course instructor(s). In all academic work to be graded, the citation of all sources is required. When collaboration or assistance is permitted by the course instructor(s) or when a student utilizes the services provided by Academic Development, the Global Communication Center, and the Academic Resource Center (CMU-Q), the acknowledgement of any collaboration or assistance is likewise required. This citation and acknowledgement must be incorporated into the work submitted and not separately or at a later point in time. Failure to do so is dishonest and is subject to disciplinary action. Instructors have a duty to communicate their expectations including those specific to collaboration, assistance, citation and acknowledgement within each course. Students likewise have a duty to ensure that they understand and abide by the standards that apply in any course or academic activity. In the absence of such understanding, it is the student s responsibility to seek additional information and clarification. Cheating includes but is not necessarily limited to: 1. Theft of or unauthorized access to an exam, answer key or other graded work from previous course offerings. 2. Use of an alternate, stand-in or proxy during an examination. 3. Copying from the examination or work of another person or source. 4. Submission or use of falsified data. 5. Using false statements to obtain additional time or other accommodation. 6. Falsification of academic credentials. Plagiarism is defined as the use of work or concepts contributed by other individuals without proper attribution or citation, but is not limited to: 1. Text, either written or spoken, quoted directly or paraphrased. 2. Graphic elements. 3. Passages of music, existing either as sound or as notation. 4. Mathematical proofs. 5. Scientific data.

6 6. Concepts or material derived from the work, published or unpublished, of another person. Unauthorized assistance refers to the use of sources of support that have not been specifically authorized in this policy statement or by the course instructor(s) in the completion of academic work to be graded. Such sources of support may include but are not limited to not limited to: 1. Collaboration on any assignment beyond the standards authorized by this policy statement and the course instructor(s). 2. Submission of work completed or edited in whole or in part by another person. 3. Supplying or communicating unauthorized information or materials, including graded work and answer keys from previous course offerings, in any way to another student. 4. Use of unauthorized information or materials, including graded work and answer keys from previous course offerings. 5. Use of unauthorized devices. 6. Submission for credit of previously completed graded work in a second course without first obtaining permission from the instructor(s) of the second course. In the case of concurrent courses, permission to submit the same work for credit in two courses must be obtained from the instructors of both courses. This policy applies, in all respects, to COURSE # J 6