Master Course Overview
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Foundation to Cell technology & 1-D simulation
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Hands-on with Li-ion cell testing, characterisation & parameter extraction
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Develop EQC based cell 1-D models for behaviour prediction (Rint, 1RC & 2RC)
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Learn Model Management, Model Standards & Simulation based testing
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Learn SIL, PIL, Embedded C, Code generation & Real time implementation
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Model based Pack Design & Cell Balancing algorithms development
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Introduction to State Estimations & Filters
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Kalman filter based SOC Estimation
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Least Square and Impedance based SOC Estimation
Career Opportunities with this Course
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BMS development algorithm engineer (R&D)
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BMS testing, validation & calibration engineer
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Cell & pack testing engineer
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BMS application engineer
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BMS Virtual validation & Software test engineer
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Cell and Battery Characterization engineer
Detailed Course Syllabus
Li-Ion Cell Chemistry & Technology
Module 1
The 1st half of Module 1 of the Master
the course covers the fundamentals of
electrochemical cells, cell constructions,
types of cell formats, cell materials, cell
chemistries, electrochemical reactions in
cells, cell terminologies, cell failure
modes and safety devices are discussed.
Some fundamental calculations on
parameters such as cell potential,
capacity and energy are also discussed by
referring to the reduction potential chart.
And the 2nd half of Module 1 describes
lithium-ion cells in more detail. Various
types of Li-ion chemistries are discussed
along with their properties and
characteristics. The fundamentals of
electrochemistry such as Fick’s law of
diffusion and Peeukert’s law are discussed
briefly. By the end of the module, the
the learner will be able to get a good grasp of
crucial electrochemical cell properties,
behavior, and characteristics.
MATLAB Scripting for 1-D Simulation
Module 2
Module 2 of the master course will cover
the topics such as foundation to Matlab
programming from basics to Advance
level. Module 2 will cover a massive amount
of tool skills such as basic math & matrix
application, import & export data, plot &
visualization of data, basic programming
concepts(if-else, loops), functions, logic
development, relational operation, etc.
module 2 also covers hands-on training
with software & provides a foundation for
career opportunities to start as a Matlab
developer. By the end of Module 2, the
the learner will be able to use Matlab software
with fluency.
MATLAB Simulink for System Modeling
Module 3
Module 3 of the master course will cover
the topics such as the foundation of the
concepts of Simulink. It involves how to
make use of blocks to construct an
algorithm or equation, how to set up the
programming constructs, logic, basic
math, etc. Further data visualization,
importing, exporting & analyzing. It is a
platform to design, develop, test & validate
the system. In module 3 we also cover
hands-on training with software & this
training will aid learners to explore career
opportunities as Model-Based System
Engineers (MBSE). By the end of module 3,
the learner will be able to use Simulink
software along with several applications
with ease.
Equivalent Circuit Based Li-Ion Cell Modeling
Module 4
In module 4, the learner uses the
knowledge acquired in modules 2 and 3 to
create a basic mathematical model of a
Li-ion cell in Matlab and Simulink using
principles of Thevenin’s equivalent circuit.
Starting off with a very basic model called
RINT, The learner first understands the
method of cell modeling (data-driven
model) and then progresses to an
the advanced model called 1 RC model. The
model is then simulated for different
current loads such as constant current
and pulse discharge. At the end of the module
4, the learner will be able to understand
how Li-ion cells are modeled and
simulated based on Thevenin's principles.
Li-Ion Cell Testing and Characterisation
Module 5
Module 5 of the master course covers the
fundamentals of Cell Testing. This module
is conducted offline. The different
standards of cell testing such as
IEC/ISO/UL/FreedomCAR and the
different categories of testing such as
static test, pulse test, thermal test, and life
cycle test are discussed. The learner then
gets hands-on experience with the cell
testing hardware (Neware BTS) and the
thermal chamber and performs the basic
cell testing activities. The learner will also
use tools such as Excel and Matlab to
perform some simple activities such as
graphical analysis, cell voltage response
analysis, capacity estimation, pulse
response analysis, internal resistance
estimation, and analyze the thermal
behavior of the cell. By the end of the
module, the learner is able to:
Understand how Li-Ion cells are tested,
Understand different procedures for cell
testing,
Analyze the test results and interpret the cell
behavior in real-time &
Create a simple program for extracting
cell parameters
1 RC Lithium Ion Cell Model Validatio
Module 6
In Module 6 of the master course, The data
obtained during the live session of the module
5 is utilized in the 1RC cell model created in
module 4. Using the data obtained and
modeling techniques from module 4, the
learner performs an important step in
modeling called Validation. Validation of
the model is done to check and verify the
model’s behavior to different inputs and to
analyze how close the model’s behavior is
to real life. By the end of module 6, the
the learner will be able to understand the
method of Model Validation and Analyze
the drawbacks of the model.
2 RC Lithium Ion Cell Model Validation
Module 7
Module 7 of the master course is a
project-based module. Using all the
knowledge acquired since module 1, The
the learner has to create a 2RC cell model
using Matlab and Simulink and validate the
model for different inputs. The results
from the 1RC model (from module 6) and
the 2RC model is compared and the
difference between the model
performance is analyzed. By the end of
module 7, the learner has a good grasp of
cell modeling techniques and model
validation.
Stateflow for Logic Driven System Modeling
Module 8
Module 8 of the master course will cover
the topics such as the foundation of the
concepts of Stateflow, how to set up
time-based events, task-based events,
trigger-based events, condition-based
events, debugging tables & charts, design
of logic & control part of algorithms.
Module 8 will also cover hands-on training
with Stateflow applications with
examples. By the end of Module 8, the
learner will be able to use Stateflow
applications in system modeling with ease.
Model Management & MBD Standards
Module 9
Module 9 of the master course will cover
the topics such as the foundation of the
Automotive software development
process (V-type model & MBSE model),
creating design & test requirements &
setting up them in Simulink, the system
architecture development, Model & data
management, concepts of MBSE project
management, data type creation & setup,
the concept of report generation of
the project, Source control, the concept model
an advisor with compliance to standards &
performance. Module 9 will also cover
hands-on training with tools such as
Simulink requirements & many others and
provides an overview of the system
engineering. By the end of Module 9, the
the learner will be able to conceptualize how to
manage & maintain the project of the
MBSE application.
Battery Management System Algorithm Modeling (Single Cell)
Module 10
Module 10 of the master course will cover
topics related to Battery Management
Systems (BMS) Algorithms & protection
functionality developments. First will start
with an understanding the basics of BMS
applications, the need for BMS modeling &
simulation, Process of model development
in the Simulink environment & will also
learn BMS protection functionalities such
as overvoltage, Undervoltage,
overcurrent, etc & estimations of cell
parameters such as SOC by coulomb &
voltage-based approach. By the end of
Module 10, the learner will be able to
understand the system modeling of BMS,
functionalities & basic estimation
methods.
Simulation Based Model Testing
Module 11
Module 11 of the master course will cover
topics related to system or algorithm
testing. It involves test requirements
setup to model, setting up test cases &
scenarios for testing, creating test
harness for unit testing, setting up a model
with different testing methods (Internal,
external & Logic), analyzing the results
with runtime analysis methods & creating
test suites for the unit to system level
testing such as simulation test, baseline
test & equivalence test. Module 11 will also
cover hands-on training with the Simulink
test & provide career opportunities in
software testing in MBSE. By the end of
Module 11, the learner will be able to use
the Simulink test tool for testing several
applications.
Model Design Verification
Module 12
Module 12 of the master course will cover
the topics related to the verification of a
designed system or model, System design
verification is pre-work that needs to be
done before moving to the code
generation part. In this module, we will
learn several concepts which deal with
identifying errors in models, algorithm
coverage, property proving concepts,
automatic test generation, etc with which
we can make sure that system or model is
ready for code generation. During the
tenure of module 12 learners will get
hands-on experience with the design
verifier tool which is part of Simulink. By
the end of Module 12, the learner will be
able to use the design verifier tool for
making the model ready for code
generation.
Code Generation - Single Cell BMS - MIL, SIL, PIL & Hardware
Module 13
Module 13 of the master course will cover
the topics such as hands-on hardware with
practical implementing & working on
Model in a loop (MIL), Software in a
loop(SIL), Processor in the loop & actual
hardware in Loop, With a deep
understanding of plant & controller model
integration, SIL testing & simulation, PIL
verification & Several approaches for code
generation. Understand code generation
requirements, setup procedure, methods
of code generation (Simulink coder,
embedded coder), monitor & tune
concepts, final deployment & testing. In
this module single-cell BMS is the target
hardware from an application point of
view. By the end of Module 13, the learner
will be able to use the Code generation tool
with ease for BMS Algorithms.
Cell Balancing Theory and Modeling
Module 14
Module 14 of the master course covers the
theory of cell balancing. Topics such as the
causes of imbalanced cells, the
requirement of cell balancing, balancing
approaches, and algorithms are discussed
in theory. After completing the theoretical
aspects of cell balancing, the cell
balancing modeling aspects are
introduced. The algorithm for cell
balancing is discussed with the help of a
flow chart, which shows the conditions
and actions necessary at each stage of cell
balancing. Tools such as Simulink and
Stateflow are used to create, test, and
simulate a basic 3-cell cell balancing
model. At the end of module 14, the learner
will be able to understand the basics of cell
balancing and is able to implement, test,
and simulate a basic cell balancing
algorithm for a 3-cell model.
Advanced Battery Protection Feature Theory and Modeling
Module 15
In Module 15, the learner applies the knowledge acquired in Modules 2 and 3 to create basic and advanced battery protection features. The learner will understand key battery parameters such as battery/cell voltage, current, and temperature, and how to control them by setting appropriate limits. By the end of Module 15, the learner will be able to comprehend and practice industry-level protection features and modelling.
Foundation to Kalman Filters
Module 16
Module 16 of the master course covers the
theory of the Kalman filter & will learn the
single-dimension application of the
Kalman filter to multi-dimension. In this
module, you learn the hands-on
calculation of the Kalman filter application
& Kalman filter flow. By the end of Module
16, the learner will be able to understand
the Kalman filter & its application.
Extended Kalman Filter Based SOC Estimation
Module 17
In module 17 of the master course, The
fundamentals of the Kalman Filter learned
in module 16 are utilized in designing an
an efficient algorithm to estimate the state of
charge of a Li-ion cell. The steps involved
in the extended Kalman filter such as the
state update equation, covariance matrix,
prediction equation and Kalman gain are
discussed. The learner uses Matlab and
Simulink to create and simulate the
designed algorithm. By the end of the
module
17, the learner has designed and
implemented a Kalman Filter algorithm for
cell state of charge estimation.
Li-Ion Cell State of Health (SOH)
Module 18
In module 18 of the master course, Another
application of the Kalman filter for cell
state estimation is explored i.e., the state of
health estimation. The fundamentals of
cell state of health estimation such as life
cycle test, capacity fading, and rise in internal
resistance, power fading, and overall cell
degradation are explored. After this, an
algorithm for the Kalman Filter for the
state of health estimation is developed
using the concepts learned in module 16.
By the end of module 18, the learner had
been designed and implemented a Kalman
Filter algorithm for cell state of health
estimation.
Advanced Algorithm Implementation on NXP Hardware
Module 19
Module 19 of the master course will cover topics such as hands-on hardware with practical implementation of cell balancing concepts, Kalman filter-based SOC estimation, impedance tracking-based SOH estimation and advanced protection logic. Final code deployment is carried out for the implementation Algorithm into target hardware(NXP-based controller). In this module, Multicell BMS is the target hardware used from an application point of view. By the end of Module 19, the learner will be able to understand the code generation for Multicell BMS for advanced algorithms & implementation into hardware.
Lab Visit
Lab Visit - 01
Li-Ion Cell Testing and Characterization
During the 1st Lab visit of our master course in Battery Management Algorithm Development, We delve into the fundamentals of Cell Testing, the diverse standards of cell testing, including IEC/ISO/UL/FreedomCAR, and explore various testing categories such as static tests, pulse tests, thermal tests, and life cycle tests. Learners engage in hands-on experiences with cell testing hardware, specifically the Neware BTS, and a thermal chamber. They actively perform essential cell testing activities, utilizing tools like Excel and MATLAB for tasks such as graphical analysis, cell voltage response analysis, capacity estimation, pulse response analysis, internal resistance estimation, and thermal behaviour analysis. By the end of the visit, participants emerge with a profound understanding of how Li-Ion cells are tested, familiarity with different testing procedures, the ability to analyze test results and interpret real-time cell behaviour, and the skill to create a simple program for extracting cell parameters.
Lab Visit - 02
Code Generation - Single Cell BMS - MIL, SIL, PIL & Hardware
In the 2nd Lab visit of our master course in Battery Management Algorithm Development, Learners will delve into hands-on hardware experiences, gaining practical proficiency in Model in a Loop (MIL), Software in a Loop (SIL), Processor in the Loop (PIL), and actual hardware in Loop. The session focuses on a comprehensive understanding of plant and controller model integration, SIL testing and simulation, PIL verification, and various approaches for code generation. Learners will explore code generation requirements, setup procedures, methods such as Simulink Coder and Embedded Coder, as well as concepts of monitoring and tuning. The final deployment and testing will be carried out, with a specific focus on single-cell Battery Management System (BMS) as the target hardware. By the end of this Lab Visit, Learners will seamlessly utilize code generation tools for BMS Algorithms, marking a significant milestone in their mastery of Electric Vehicle (EV) technology.
Lab Visit - 03
Advanced Algorithm Implementation on NXP Hardware
During the 3rd Lab visit of our Battery Management Algorithm Development master course. Learners delve into practical hardware implementation, mastering cell balancing, Kalman filter-based State of Charge (SOC) estimation, impedance tracking-based State of Health (SOH) estimation, and advanced protection logic. The pinnacle of learning is reached as the final code is deployed into NXP-based controllers, specifically the Multicell Battery Management System (BMS). This hands-on experience equips learners with the expertise to comprehend code generation for Multicell BMS, enabling the implementation of advanced algorithms into real-world hardware. By the end of this lab visit, participants emerge with a comprehensive understanding and practical proficiency in advanced battery management algorithms, marking a significant milestone in their mastery of Electric Vehicle (EV) technology.
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Course Pricing Flexibility
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Master Certification Course in Battery Management Algorithm Development
The course fee is ₹1,50,000 + ₹27,000 (18% GST) = ₹1,77,000 (Paid in 12 EMI's) (With placement assistance)
12 x ₹14,750.00
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