Course 2: Energy Storage Systems for E-Mobility

Learn Lithium Ion Battery Technology and Energy Storage Systems for Electrified Vehicles

 

2 - 4 July 2018

Lecturer: Prof. Marcello Canova, The Ohio State University

Course can be taken completely (Day 1 to 3, Course 2) or just Day 1 for an overview of the Topic (Course 2a). 

Target Group

The intended audiences for this course are:

 

  1. Automotive industry executives who wish to become acquainted with energy storage systems and Lithium Ion battery technology for electrified vehicles.
  2. Automotive industry engineers and engineering managers with an interest in electrified powertrains, who wish to become better acquainted with the operating principles, modeling, experimental techniques, system integration and control aspects related to battery packs for electromobility.

 

The course has been designed so that executives, managers and more senior engineers who may wish to be exposed to the foundations and to the language of Lithium ion batteries and energy storage systems may gain sufficient familiarity with the subject matter by attending Day 1 only.   Days 2 and 3 delve more deeply into technical topics, so that attending the full three days will make the participant closely acquainted with models, experimental characterization methods, as well as the most important estimation and control algorithms utilized in Battery Management Systems (BMS).


The course requires mechanical and electrical engineering background. Dipl.-Ing. / M.Sc. level mechanical or electrical engineering degree is preferred, but B.Sc. graduates with automotive engineering experience are also encouraged to attend.


More information about Prof. Canova

Course Objectives

The course introduces the participants to energy storage systems for electrified vehicles based upon Lithium Ion battery technology. The course is designed to provide engineer and managers in the Automotive Industry a broad overview on the subject, covering multiple areas such as cell materials and fundamental properties, testing procedures for performance characterization, modeling and simulation, system integration, control, diagnostics and prognostics. The course is a combination of lectures, case studies and computer laboratory exercises. During the laboratory sessions, the participants will utilize Matlab/Simulink tools to design and calibrate a Lithium ion battery model, develop a State of Charge estimation algorithm and a simple strategy for battery pack charging and balancing.


The course is available in two formats (three-day, Course 2, and one-day, Course 2a). The broad objectives apply to both versions, but the specifics can be found below:

 

Objective 1     

(Courses 2 and 2a):

Explore the state of the art of lithium ion batteries for automotive applications, their operating principles, materials, and future directions of technical development.

Objective 2

(Course 2 and 2a): 

Learn principles of system integration for Lithium ion battery packs, such as design and packaging, Thermal Management Systems (TMS), and Battery Management Systems (BMS) operating principles and hardware architectures.

Objective 3

(Course 2): 

Develop competence in understanding and utilizing mathematical models to simulate the behavior of lithium ion batteries (voltage-current relationship) and the mechanisms that lead to performance degradation (aging).

Objective 4

(Course 2): 

Learn the experimental methods, equipment and procedures required to characterize the performance and degradation processes in battery cells.

Objective 5

(Course 2):

Utilize mathematical models to develop simple algorithms for SOC estimation, pack charging and balancing, similar to those commonly implemented in Battery Management Systems.

Course Outline

Day 1, Part 1:

State of the art of lithium ion batteries for automotive applications, operating principles, materials, and future directions of development.

Day 1, Part 2:

Principles of system integration for lithium ion battery packs, Thermal Management Systems (TMS), and Battery Management Systems (BMS).

  • Introduction to fundamental concepts and terminology;
  • Working principles and operation of a battery cell;
  • Overview of lithium ion batteries for automotive applications and typical characteristics
  • Basic principles of applied electrochemistry for battery cells (open-circuit potential, activation overpotential, losses due to Ohmic and transport effects).
  • Introduction to battery packs for automotive applications, with focus on design, components and system challenges (integration, packaging, controls, safety);
  • Overview of Battery Management Systems (BMS), software/hardware architectures;
  • Overview of Thermal Management Systems (TMS): passive and active cooling methods.
  • Introduction to State of Charge (SOC) and State of Health (SOH) estimation methods.

Day 2, Part 1:

Overview of experimental methods for characterization of cell performance and degradation.

Day 2, Part 2:

Mathematical models for simulation of lithium ion batteries, with focus on performance (voltage-current relationship) and aging (degradation mechanisms).

  • Introduction to instrumentation and experimental procedures;
  • Electrochemical analysis methods (CV, GITT, EIS) and microscopy/visualization methods (SEM, TEM);
  • Cell-level and system-level analysis methods, equipment and protocols for characterization of cell/pack dynamic performance.
  • Introduction to USABC Testing Protocols for battery performance and life characterization.
  • Classification of battery models;
  • Heuristic methods, equivalent circuit models;
  • Electrochemical models: Pseudo 2D (P2D) model, single particle (SP) model, extended single particle (ESP) model;
  • Modeling heat generation and thermal dynamics in a battery cell;
  • Modeling battery packs including cell to cell variability.
  • Modeling/simulation exercises: Develop and calibrate equivalent circuit cell model using Matlab/Simulink. Analyze, tune and verify electrochemical (SP) model.

Day 3, Part 1:

Principles and algorithms for SOC and SOH estimation.

Day 3, Part 2:

Principles and algorithms for pack charging and balancing.

  • Overview of SOC estimation methods, heuristic vs. model-based techniques;
  • Basic principles of feedback control and state observer design, Extended Kalman Filters;
  • Introduction to battery life assessment methods and SOH;
  • Physical phenomena leading to cell performance Degradation;
  • Modeling/simulation exercises: Build and compare different SOC estimators in Simulink. Build and tune an Extended Kalman Filter (EKF) SOC estimator.
  • Basic principles and overview of algorithms for pack charging and balancing (passive vs. active);
  • Modeling/simulation exercises: Design and optimization of CC-CV charging algorithm. Design of simple passive balancing algorithm.  Simulation models (Matlab/Simulink) will be provided.

Goals

Upon completion of this course, the participants will possess practical knowledge of

(1) Operating principles and characteristics of Lithium-ion batteries, including the effects of electrode/electrolyte materials on performance and durability;

(2) Experimental methods for characterizing performance and life of Li-ion cells, in support of modeling, design and prototype verification;

(3) Modeling and simulation tools to solve system-level design and optimization problems for battery packs for EVs and HEVs;

(4) Common system integration and control issues (electrical and thermal management, state estimation, etc...), and solution methods.

For registration click here.