internship project topics - summer 2018 - mit energy club

1 1/8/2018

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KPIT Research ProjectsFor Summer 2018 Internships with Premier Institutes

© KPIT Technologies Limited

Summer 2017 Internship experience at KPIT Technologies

1/8/20182

Output:- Algorithms, Simulation Models, Reports and 5 International Papers (underway)

.

IIT Bombay

1

IIT Roorkee

3

Stanford

University

2

IIT Madras

1

IIT

Kharagpur

2

IIT Kanpur

5

EV Thermal

Management

Model

Predictive

Control for HEV

Machine//Deep

Learning

EV Powertrain

Control

Advanced

Computing

Research Areas

© KPIT Technologies Limited1/8/20183

Electric Vehicle Management System


Heat generation and dissipation models for electric vehicle systemBackground :- In an electric vehicle, thermal management of key components is very important. To design appropriate energy management schemes, it is important to quantitatively model the heat generated and dissipated at individual components of the EV system, and accumulate at the system level.

Project Scope*:- Develop heat generation and dissipation models for the EV system components..

• Develop mathematical models for heat generation and dissipation and implement in MATLAB/Simulink

for :

– Batteries

– Power converters

– Motors

– Transmission

– Vehicle (includes drag, tyre friction etc. )

• Model validation

– Validate the models against real measurement data from EV

1/8/20184

* The project scope may vary depending on internship duration.

Deliverable:- Transient Simulink/Simscape mathematical model with project report


Transient Thermal Mathematical Model for Electric Vehicle with controls

Background :- In an electric vehicle, thermal management of key components is very important and to understand

and optimize the same we need to capture key parameters in form of model which is predictive transient and control

enabled.

Project Scope*:- Develop transient mathematical model for EV in following areas.

1/8/20185

• Battery Thermal Management

– Air cooled system

– Liquid cooled system with passive method

– Liquid cooled system with active cooling system (Vapor compression)

– Validation of model with test data

– Consider all modes of heat transfer and control cycle.

• Motor Cooling

– In addition to above Battery Thermal Management following are additional points.

– Model complexity of conjugate heat transfer from metal to coolant and coolant to air.

– Model Thermal inertia with best accuracy.




Mathematical Model of EV Climate Control System

• Develop Transient model of vehicle cooling load calculations with

– Variable human and vehicle load inclusive of sensible, latent and radiative components.

– Variable ambient temperature, humidity and atmospheric pressure load.

– Variable running conditions of vehicle.

• Develop model of HVAC system to suit above load using

– Conventional vapour compression system

– Active and Passive evaporative cooling

– Optimize above models for power consumption and performance.

– Validate with test data

1/8/20186

Background :- In an electric vehicle, Climate Control is one very important for passenger comfort and is also power

consuming. In order to identify and optimize key parameters to enhance comfort with reduced power consumption

we need to have a predictive and efficient mathematical model for the same.

Project Scope*:- Develop transient mathematical model for EV in following areas.




Development of high fidelity Li-Ion battery model

1/8/20187

• Develop the battery model

– Develop the battery model based on chemical kinetics and equivalent electrical components.

– Parameterize the battery model, and incorporate all usually occurring faults.

– Simulate and identify all the nominal and faulty operating conditions and corresponding battery parameters.

– Analyse the model behaviour under nominal and faulty operating conditions.

• Develop diagnosis and prognosis schemes for battery

– Develop algorithms for diagnosis of faults.

– Monitor parameter degradation for batteries.

– Predict the RUL (Remaining Useful Life) of batteries.

• Validation of modeling and diagnosis schemes

– Validation by simulation using existing EV model under different drive cycles.

– Validation using real data from EVs

Background :- Using a high fidelity battery model a lot of time/expenses can be saved while trying to understand

the dynamic characteristics for prediction of SOC/SOH dynamics, analysis for diagnostics, predicting the range of

vehicle under various operating/driving conditions, characterizing the battery, etc.

Project Scope*:- Development of high fidelity Li-Ion battery model

Deliverables:-• MATLAB scripts and Simulink models for battery

• Scripts for diagnosis and prognosis schemes

• Project report detailing modelling, diagnosis, prognosis and validation.



Fault analysis of EV through simulation

1/8/20188

• Identify the fault modes of individual subsystems/components in the EV in different EV architectures– Simulate EV models for all EV configurations under nominal mode and different fault modes

– Identify key variables indicating fault symptoms

– Prioritize faults based on levels of severity

– Automate the simulation of faults by identifying (through script) the individual components and interconnections in an existing simulation model, and changing their fault modes.

• Build dependency graphs/tables between component fault modes and system variables– Develop fault dependency graphs and/or tables based on the effect of different faults on key system variables

– Validate the graphs based on real data as much as possible from experiments

• Automated diagnosis scheme based on data generated through fault simulation– Apply model based and data driven fault detection, isolation and identification schemes on fault data generated through

simulation

– Validation of fault diagnosis scheme using real fault data

Background :- In an electric vehicle, there are various electrical and mechanical faults that could happen. It is

impossible to replicate all fault scenarios practically. Hence, it is important to investigate the effects of faults in

individual components on other components and the system as a whole using simulation models. The fault

propagation effects can be represented in appropriate forms such as graphs, and can help in diagnosis.

Project Scope:- Fault analysis of electric vehicles through simulation studies

Deliverables:-• MATLAB scripts for fault simulation.

• Project report .



Real time simulation techniques for Electric vehicles

• Employment of best coding practices in the existing EV models for maximum computing resource

utilization

• Exploration of real time operating systems and virtual real time environments for running the models

• Utilization of GPUs and cloud computing resources for simulation of models.

1/8/20189

Background :- Development of performance analysis, control and diagnostic strategies for EVs might require

complex models for batteries, converters, motors, transmission and vehicle. The models have to be simulated

for all possible drive conditions and fault scenarios for adequate test depth. This requires optimization of the

models for real time amenability without compromising fidelity and optimal utilization of computing

resources. Models have to be reorganized for exploiting parallel computing resources when available. On the

computing platforms, different operating systems and usage of GPU and cloud computing for such

simulations should be explored.

Project Scope*:- Real time simulation techniques for electric vehicles


Deliverables:-

• Scripts used for simulations

• Project report.


Sizing of electric vehicle components for optimal efficiency

1/8/201810

• Determine the optimal parameters and placement of EV subsystems for improving electrical and

mechanical efficiency

– Enhance the existing EV model to capture vehicle dynamics and environmental conditions in detail

– Formulate an optimization problem for overall EV system efficiency considering all the relevant parameters of individual

components.

– Using the simulation model for EV, find the optimal values of component parameters by using nonlinear optimization

techniques

Background :- Increasing the electrical and mechanical efficiency of EVs help increasing the range and reducing the

charging intervals, as well as reducing the battery packs required. The overall efficiency of EV depends on a number of

parameters related to individual components such as batteries, power converters, motors and transmission. It is

required to optimize each of these component parameters for specific EV sizes and configurations to maximize

efficiency.

Project Scope*:- Sizing of electric vehicle components for optimal efficiency

Deliverables:-• MATLAB/Simulink scripts and models for EV

• Scripts for optimization of efficiency and determination of parameters.

• Report detailing the optimization procedures.



Supervisory control structures for EVs

Background :- The supervisory controller in EV is required to generate the torque demand set point for the electric

motor. This is based on throttle input, speed etc. subject to several constraints on battery SOC, voltages, currents,

temperatures, response time etc. In order to satisfy the control performance requirements while maintaining the

constraints, advanced control strategies such as Model Predictive Control (MPC) might be needed in supervisory

controllers.

• Development of advanced control schemes for supervisory control in EV

– Identification of controlled variables, manipulated variables and constraints for EV control for safe operation

and optimal performance.

– Formulation of supervisory control problem for EV in terms of controlled variables, manipulated variables

and constraints, with associated cost function.

– Development of advanced control schemes for supervisory control of EV based on the above formulation.

– Performance comparison of developed control schemes with conventional control schemes.

1/8/201811

Project Scope*:- Supervisory control structures for EVs

Deliverables:-• Scripts used for simulations

• Project report.



Virtual sensing for EV control applications

1/8/201812

• Development of virtual sensing schemes for unmeasured signals in an EV system

– The sensing schemes can make use of the EV system model and estimation schemes for computation of signals.

– Different sensing schemes might be implemented and compared for performance

– Validation of virtual sensing shall be performed with real measurement data.

• Illustration of improvement in control performance using virtually sensed signals

– Using the virtually sensed signals, the performance of control algorithms should be evaluated for indicators such as

sensitivity and robustness

Background :- The controllers in an EV system require various signals for computation of control logic. For example,

the vehicle controller requires velocity of the vehicle, and the brake controller requires brake pedal position. Not all of

the required variables may be available as direct measurements or some may be unreliable. Soft sensing techniques

such as Kalman filtering can be used for computing some of the required signals, in conjunction with a dynamic

model of the vehicle.

Project Scope*:- Virtual sensing for EV control applications

Deliverables:-• Scripts/models in MATLAB/Simulink for virtual sensing and models in Simulink for controller

implementation for the vehicle system

• Project report detailing virtual sensing schemes and controller validation



Autonomous Vehicle / ADAS projects


Sensor Fusion platform for autonomous vehicles

1/8/201814

• Sensor Fusion platform for autonomous vehicles

– Data processing of typical sensors like ultrasound, Radar, LiDar and Camera

– Use of techniques such as Kalman filter, convolution neural networks, fuzzy logic etc. for data fusion from these sensors to re-create 360° view of surroundings around the vehicle

– Comparison of fusion schemes for performance

– Feasibility Analysis

– Design by simulation

– Validation using real data

– Prototype development

Background :- There could be several measurement sources for control purposes in an autonomous vehicle, such as

ultrasound, Radar, LiDar and camera. For better reconstruction of the vehicle surroundings, combining the data from

multiple sensors to create cleaner signals is advantageous. Kalman filtering, neural networks etc. can be used for such

sensor fusion.

Project Scope*:- Sensor Fusion platform for autonomous vehicles

Deliverables:-• Scripts/models used for simulation studies

• Project report.



Deep learning library on tiny Embedded platform

1/8/201815

• Embedded platform for Deep learning

– Task Specific Libraries

– Lower execution Time

– Small footprint

– Highly efficient

Background :- Deep learning algorithms require a lot of libraries. These libraries have many computing approaches

and need high speeds for the execution in short time. Traditionally GPU’s are used to perform these tasks, however to

make cost effective solution, alternative solutions or task specific libraries need to be built.

Project Scope:- Design and develop optimized deep learning libraries.

Deliverables:-• Optimized deep learning libraries.

• Project report.


Tiny computing platform for Deep learning

1/8/201816


– Camera Input

– Sensor Input

– Network connectivity

– Deep learning algorithm processing capability

– Small form factor

Background :- There could be several high end computing machines available for the deep learning, local or cloud

based. However the challenge is the vehicle, where the application is very specific and has no room for high end

onboard computing. It is required to develop cost effective, low end computing platform for deep learning onboard

the vehicle.

Project Scope:- Design and develop a Small factor embedded system for deep learning.

Deliverables:-• Architectural Design of the system

• Project report.


Tiny Embedded platform Lidar

1/8/201817


– Receive Lidar data over Ethernet

– Lower execution Time

– Small footprint

– Highly efficient

– Quick detection

Background :- Lidar is the primary sensor in an autonomous vehicle. The challenge is how to use Lidar data to

extract useful information using a small embedded platform. Currently, processing lidar data requires high end

computing platform as generated data is very large.

Project Scope:- Design and develop a small Embedded platform for lidar data processing.

Deliverables:-• Architectural design and Prototype on development board.

• Project report.


SLAM and DATMO for automotive applications

Background :- For autonomous navigation of vehicles, it is important to have good perception of surroundings,

both static and dynamic. If the maps (static part) do not exist or are not up to date, the maps have to be created on

the fly and used for path planning. The dynamic objects also need to be identified and tracked for successful

navigation.

Project Scope*:- Generate grid map using Simultaneous Localization and Mapping (SLAM) and then detect and

track moving objects (DATMO).

1/8/201818

• Grid Map

– Lidar and Radar data processing

– Occupancy based grid map

– Ego motion estimation and map update

• Moving Obstacles

– Categorize objects as static and dynamic .

– Classify dynamic objects as pedestrians, vehicles, etc

– Track dynamic objects as per the class specific motion model.

Deliverable:- Source code and documentation



Marker-less online calibration

Background :- Autonomous vehicles have multiple sensors and sensor fusion is an important task to improve

robustness of perception of the surroundings. The extrinsic calibration of these sensors is critical for sensor fusion and

needs to be kept correct even when there is relative physical movement between them.

Project Scope*:- Develop an online marker-less extrinsic calibration solution.

1/8/201819

• Extrinsic Calibration

– 6 DOF are involved and need to be estimated

– Need to estimate them for sensors like cameras, Lidars, etc

– Online calibration while vehicle is moving. Not in factory or in garage.

• Marker-less Calibration

– Cannot rely on known calibration targets like checker boards.

– Online miscalibration detection

– Automatic calibration tracking.

Deliverable:- Source code and documentation



Power Train


Power Line Communication (PLC) for EV charging

• Develop SPI based communication interface from Microcontroller to QCA7005 Qualcomm chip

– SPI protocol development and testing for Smart charge communication packets.

– Interface logic to send/receive this data to/from Qualcomm chip.

• Configuration of Qualcomm chip for PLC SLAC based communication on Qualcomm QCA 7005 chip

– PIB file configuration

– Flash programming logic

– SLAC protocol configuration

– Testing

1/8/201821

Background :- In electric vehicle, Powerline Communication is used for communication between electric vehicle

charging station (EVSE) and plug-in electric vehicle (PEV). Power line communication (PLC) is a technology designed to

use the existing power lines and support communications. An ISO/IEC 15118 standard is pushing to standardize PLC

as a technology that supports EV charging systems.

Project Scope*:- Develop PLC interface for EV-EVSE communication for Smart charging.

Deliverable:- PLC communication software and hardware interface with project report



OBD Monitors for Conventional/HEV/EV powertrains

1/8/201822

Background:

OBD Monitor control's development has to address the multiple requirements from the legislation, creation of a

requirement library, based on input and interfaces from engine EMS signal can be advantageous to create a requirement

framework and validation of the set of requirements for OBD monitors

Project Scope:

▪ OBD monitor requirements for conventional powertrain and diagnostics requirements specific to individual

components and overall vehicle level for HEV / EV powertrain

▪ OBD monitor requirement gathering as per various global legislations for conventional powertrain

▪ Mapping the diagnostics requirements, when the powertrain application is varied with different HEV / EV configuration

Project Deliverables:

▪ Requirement library for select number of OBD monitors for conventional powertrain

▪ Requirement library for Diagnostics and legislation requirements of HEV / EV powertrain as per various global

standards

Assumptions:

▪ Engineers are from automobile background, willing to take this work as their internship program with minimum

supervision / inputs

▪ KPIT shall invest in required standards and literature and not only depend on the public data available on internet


Gasoline Engine EMS - Control Strategy development

1/8/201823

Background:

Low cost development of the control strategy and proving it on the a proto EMS can address various Tier1 companies,

which are in to manufacturing of the engine sensors and actuators to partner with KPIT

Project Scope:

▪ Gathering / benchmarking the engine data from internet

▪ study of patents on gasoline engine EMS and control software's

▪ Creation of control strategy for Air Flow, Fuel Injection, Spark Control

Project Deliverables:

▪ Control strategy for Gasoline engine EMS functions

▪ POC running on Proto engine and Engine interfaced with HIL setup

Assumptions:

▪ Engineers are from automobile background, willing to take this work as their internship program with minimum

supervision / inputs

▪ Engine, Test facility and HIL setup will be available either with outsourced location or with institutes

▪ KPIT shall invest in required standards and literature and not only depend on the public data available on internet


Diagnostics Projects


Vehicle Diagnostic ChatBot

Background :- Vehicle diagnostic is becoming more and more important with increasing electronics in the vehicles.

As a vehicle user, many times we face problems which could get solved without taking the vehicle to workshop. Can

Vehicle Diagnostic ChatBot backed-up by appropriate vehicle diagnostic model, be used in such scenarios ?

Project Scope*:- Develop vehicle diagnostic ChatBot

1/8/201825

• Develop ChatBot application

• NLP (natural language processing) engine to analyse inputs from vehicle user.

• Enhance existing vehicle diagnostic model to make it context aware.

• Deploy the diagnostic model on cloud.

• Develop mobile app for the ChatBot

• Interface ChatBot with the diagnostic model.

• Create known faults and train the ChatBot.

Deliverable:- Vehicle Diagnostic Chatbot implemented for a specified vehicle.



CyberSecurity Projects


Development of SHE+ library for next-gen Aurix

Background :- Secure Hardware Extension or SHE is functional specification of a minimalistic hardware co-

processing unit. SHE follows the approach of bringing a fixed and self-contained functionality in the form of an on-

chip hardware extension of a regular micro-controller. The purpose of SHE is to separate symmetric keys from ECU

applications. Its core functionality is the storage of symmetric keys and basic operations such as encryption,

decryption, etc., with these keys. It offers protocols and processes to allow for a secure injection of keys into the ECU

by providing end-to-end security from backend.

Project Scope*:- SHE library for next generation Aurix board.

1/8/201827

• Develop SHE+ library and crypto algorithms

– Build SHE+ library on top of HSM drivers

– Develop Crypto algorithms to run on HSM core (ARM)

Deliverable:-SHE+ library integrated with HSM and crypto algorithms



Certificate Handling for automotive embedded systems

• Develop certificate handling infrastructure

– Generate secret keys

– Build secure storage to store secret keys

– Certificate Authority or CA to issue certificates.

– Root chain validation of certificates

– Manage security profiles

– Public Key Infrastructure or PKI

1/8/201828

Background :- Number of electronic devices in modern cars are increasing day-by-day. Also, cars are connecting to

other cars, infrastructure, cloud, etc. Each of these external interface is a potential attack surface. So, electronic devices

(or ECUs) running inside a cars need a mechanism to authenticate an external device before connecting. Digital

Certificates are one popular form of authentication used by computer industry. The same can be adopted in

automotive industry to validate the authenticity of connecting devices. Creation of certificates, provisioning, issuing,

renew, revoke, etc., are needed to manage certificates. Also, a secure storage is needed to store the keys. Also, many

ECUs will need certificates. So, a Public Key Infrastructure or PKI needs to be developed to handle certificates .

Project Scope*:- Development of certificate handling infrastructure.

Deliverable:- Certificate handling infrastructure



Light Weight Cryptography for Automotive

– Light Weight Block Ciphers

– Light Weight Stream Ciphers

– Light Weight Hash Functions

– Light Weight Message Authentication Codes

1/8/201829

Background :- Number of Electronic Control Units or ECUs inside a car are increasing rapidly. These advancements

have also thrown up challenges to security of the cars internal networks. Traditionally computer networks use

cryptography to secure the networks. But, ECUs are low powered micro controllers. Running sophisticated

cryptographic algorithms inside an ECU will affect the performance of the ECU. Some critical applications inside a car

need real time performance and also good security. Hence light weight cryptography assumes importance. These light

weight cryptographic algorithms are aimed to providing good security while not affecting the performance of the

ECU.

Project Scope*:- Implementing Light Weight Cryptographic algorithms for automotive embedded systems

Deliverable:- Light Weight Crypto Library and related applications



Elliptic Curve Cryptography for Automotive

– Generation of asymmetric keys (public key and private key)

– Key exchange using Elliptic Curve Diffie–Hellman or ECDH

– Elliptic Curve Digital Signature Algorithm or ECDSA

– Client Applications

1/8/201830

Background :- Elliptic Curve Cryptography or ECC is gaining importance in embedded systems due to various

advantages over the traditional RSA. The major disadvantage of RSA is, it needs longer keys for higher security, which

directly affects the performance. But, ECC can achieve the same level of security with smaller key lengths with lesser

impact on performance. At 128-bit security levels RSA is 10 times slower than ECC for performing private key

operations such as signature generation and key management, At 256-bit security level RSA is 50 to 100 times slower.

The public and private keys generated by RSA takes up more space than ECC. In embedded systems, sessions will be

shorter and each time a session starts keys need to be generated. So, ECC is becoming preferred algorithm for

Automotive Embedded Systems,

Project Scope*:- Implementing ECC for automotive cyber security

Deliverable:- ECC Library and client applications



Thank You

www.kpit.com

internship project topics - summer 2018 - mit energy club

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