Hybrid Electric Vehicles

    LTC Combustion Engines

    Fuel Flex Powertrains

    Buildings in Smart Grid

    Members


 

Energy Mechatronics Laboratory (EML) focuses on increasing efficiency of energy systems through utilization of advanced techniques of control, modeling, estimation and diagnosis. Our research includes both theoretical and experimental aspects of energy systems, ranging from theoretical modeling of the systems to real-time implementation of energy controllers.

Current research involves the transportation and building sectors which account for 68% of total consumed energy in the United States. In the following, a brief overview of some of our ongoing research projects are provided.

HCCI Principle
Some of current research areas in the EML

EML Sponsors

Current EML's sponsors / collaborators

 

Hybrid Electric Vehicles

LTC Combustion Engines

Fuel Flex Powertrains

Buildings to Grid Integration

 

Hybrid Electric Vehicles (HEVs)

Hybrid electric powertrain using a fuel adaptive LTC engine

Current HEVs use spark ignition or compression ignition (diesel) engines. LTC engines have a higher thermal efficiency than typical gasoline and diesel engines. In addition, nitrogen oxides and soot emissions are negligible in LTC engines. Designing HEVs which utilize LTC engines at their optimum operation points will significantly improve fuel economy of HEVs while maintaining low vehicle emissions.

One major challenge for realizing LTC HEVs is control of complex behavior of LTC engines during transient operation. This challenge can be tackled by designing control strategies which utilize an electric motor to provide required torque during transient operation and relying on an LTC engine for steady-state mid-to-high load operation. Our research focuses on modeling and mode-based control of energy management in LTC HEVs. The domain of this research covers a large range of HEVs including plug-in hybrid drive-trains.

HEV Powertrain Test Cell

EML's developed LTC-HEV powertrain test cell at Michigan Tech's APSRC, using a 465 hp double-ended dynamometer

Model-based condition monitoring of components in hybrid powertrains

Our research focuses on model-based condition monitoring of hybrid powertrains. Observer based fault detection techniques are developed to increase reliability of hybrid powertrains and also to address on-board diagnostics (OBD) requirements. In addition to physical models, embedded dynamic neural network monitoring models are developed to identify a faulty change in system parameters. These techniques will be applied for condition monitoring of components in hybrid powertrains (e.g. condition monitoring of battery state-of-health).

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LTC Combustion Engines

LTC combustion control

Low temperature combustion (LTC) includes lean burn or highly diluted, advanced combustion modes with combustion temperature typically below 1800 K (i.e., NOx formation temperature). These low-NOx combution modes have the hybrid features of spark-ignited and diesel engines. Similar to spark-ignited engines the air-fuel charge is premixed or partialy premixed, and similar to diesel engines the air-fuel mixture is ignited through compression ignition. The LTC engines have faster burning rate compared to conventional engines, leading to some of the highest recorded thermal efficiency (thus low CO2 emissions). In addition, particulate matters (PM) is negligible in the LTC engines since combustion regime is mostly premixed. The term LTC encircles a family of engine technologies including reactivity controlled compression ignition (RCCI), premixed charge compression ignition (PCCI), and homogenous charge compression ignition (HCCI).

Fuel Flex Powertrain

LTC engine researchers along with the experimental setup at APSRC

Proper operation of LTC engines requires an in-depth understanding of the combustion and development of practical control techniques to optimize engine combustion particularly during transient engine operations. EML scholars are working on developing computationally efficient LTC combustion models that can be used for control applications. The combustion models are then used to develop within-cycle or next-cycle combustion control strategies.

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Fuel Flex Powertrains (FFPs)

Intelligent control of fuel flex powertrain in an adaptive framework

Current vehicles only work with a certain range of fuels for which the internal combustion engine is calibrated. Future advanced drive-trains should be fuel flexible and should address the transition towards a range of renewable/alternative fuels. Our research centers on design of drive-train control strategies that can adapt to variable fuel chemistries.

Fuel Flex Powertrain

Control framework of a fuel adaptive powertrain

Novel adaptive techniques for on-board fuel parameter estimation will be developed. The estimated fuel parameters are combined with adaptive drive-train control techniques in order to optimize fuel consumption and to decrease exhaust emissions. This research investigates a range of fuel types including oxygenates (butanol and ethanol), biodiesel, natural gas, hydrogen, and synthetic gas. The main goal is to increase fuel flexibility of drive-trains, so they can run with fuel sources which are locally available at the locations where the vehicles are utilized.

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Buildings to Grid Integration (B2G)

Adaptive energy control of building in smart grid

Heating, ventilation and air-conditioning (HVAC) systems consume over 60 percent of energy in buildings and over 92 percent of energy in commercial buildings. Energy-optimal operation of HVAC systems can significantly reduce building energy usage, decrease peak electrical demand, and lower building carbon dioxide emissions. In our research, advanced model-based control techniques are developed to optimize building energy usage while maintaining conditions to meet human comfort and address CO2 emission constraints.

Building parameters such as thermal capacity of walls vary from one building to another. The variability in building model parameters causes a major challenge for designing accurate model-based energy controllers. Adaptive parameter estimation techniques are developed for real-time identification of building parameters to remove model deficiencies. Adaptive HVAC controllers, integrated with real-time parameter estimators, are designed to optimize energy consumption in buildings.

Building interaction with smart grid

Interaction of the adaptive buidling controller with smart grid

Connection of a building to a smart grid brings a challenging opportunity for the building adaptive energy controller. The controller can optimize both the energy use and the energy cost, and also reduce the total energy demand of the building in the peak hours. The controller should consider electric load variation in the building and energy price variation in the smart grid. An optimal self-tuning control framework is designed to minimize energy consumption in buildings.

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Members (thesis advisees)

MohammadReza Amini

Mohammad R. Amini
(PhD; 09/13 - )
[Easily Verifiable Control Design]

Amir Khameneian

Amir Khameneian
(PhD; 5/16 - )
Co-advised by
Prof. Jeffrey Naber
[Engine Controls]

Nithin Teja Kondipati

Akshat Abhay Raut
(MSc; 7/16 - )
[LTC Engines]

Behrouz Khoshbakht

Behrouz Khoshbakht
(PhD; 9/14 - )
Co-advised by
Prof. Jeffrey Naber
[Engine Controls]

Joe Tripp

Joe Tripp
(PhD; 11/16 - )
Co-advised by
Prof. Darrell Robinette
[Connected Vehicles]

Post Doctoral Researchers

Hamit Solmaz

Dr. Hamit Solmaz
(Postdoc; 4/15 - 3/16)
[LTC Engines]

Meysam Razmara

Dr. Meysam Razmara
(Research Engineer; 08/16 - 01/17)
Co-supervised by
Prof. Rush Robinett III
[Building-Grid]

Short Term Scholars

Drew Hanover

Drew Hanover
(BSc; 10/15 - )
[Building]

Sid Shah

Sid Shah
(MSc; 02/17 - )
[LTC Engines]

Sourabh Kulkarni

Sourabh Kulkarni
(MSc; 02/17 - )
[Connected Vehicles]

Rajeshwar Yadav

Rajeshwar Yadav
(MSc; 02/17 - )
[Connected Vehicles]

Alumni - Thesis Students

PhD Graduates

Mehran Bidarvatan

Dr. Mehran Bidarvatan (PhD; 09/12 - 12/15)
Thesis
First affiliation after graduation:
Automotive Industry

Meysam Razmara

Dr. Meysam Razmara
(PhD; 10/12 - 07/16)
Thesis
Co-advised by
Prof. Rush Robinett III
First affiliation after MTU:
AIP - Hawaii

Boopathi Mahadevan

Dr. Boopathi Mahadevan
(PhD; 1/14 - 01/17)
Thesis
Co-advised by
Prof. John Johnson
First affiliation after graduation:
Cummins

Ali Solouk

Dr. Ali Solouk Mofrad
(PhD; 09/13 - 02/17)
Thesis
First affiliation after graduation:
Ford

MSc Graduates

Vishal Thakkar

Vishal Thakkar
(MSc; 01/13 - 8/14)
Thesis
First affiliation after graduation:
Ford

Deepak Kothari

Deepak Kothari
(MSc; 05/13 - 8/14)
Thesis
First affiliation after graduation:
Chrysler

Hrishikesh Saigaonka

Hrishikesh Saigaonkar
(MSc; 05/13 - 10/14)
Thesis
First affiliation after graduation:
Cummins

Meysam Razmara

Meysam Razmara
(MSc; 10/12 - 09/14)
Report (Plan B)
First affiliation after graduation:
PhD student at
Michigan Tech.

Madhura Paranjape

Madhura Paranjape
(MSc; 05/13 - 12/14)
Thesis
First affiliation after graduation:
General Motors

Kaveh Khodadadi

Kaveh Khodadadi
(MSc; 09/13 - 04/15)
Thesis
First affiliation after graduation:
PhD student at Ohio State Univ.

MohammadReza Nazemi

Mohammad R. Nazemi
(MSc; 09/13 - 05/15)
Thesis
First affiliation after graduation:
PhD student at Georgia Inst. of Tech.

Ali Solouk

Ali Solouk Mofrad
(MSc; 09/13 - 10/15)
Report (Plan B)
First affiliation after graduation:
PhD student at Michigan Tech.

Jeremy Dobbs

Jeremy Dobbs
(MSc; 05/13 - 12/15)
Thesis
First affiliation after graduation:
Tannas Co.

Kaushik Kannan

Kaushik Kannan
(MSc; 9/14 - 07/16)
Thesis
First affiliation after graduation:
Ford

Jayant Arora

Jayant Arora
(MSc; 1/15 - 07/16)
Thesis
First affiliation after graduation:
Cummins

Nithin Teja Kondipati

Nithin Teja Kondipati
(MSc; 9/15 - 07/16)
Thesis
First affiliation after graduation:
Fiat Chrysler Automobiles

Alumni - Short Term Scholars

Barzin Moridian

Barzin Moridian (MSc)
(10/12-3/13)
[Building Estimation]

Abhishek Kondra

Abhishek Kondra (MSc)
(1/13-5/13)
[LTC Engines]

Zhao Han

Zhao Han (Msc)
(01/13 - 10/13)
[HEV]

Fouad Ahmed

Abhijit Girase (MSc)
(05/13 - 9/13)
[LTC Engines]

Hao Su

Hao Su (MSc)
(09/13 - 10/13)
[LTC Engines]

Fouad Ahmed

Fouad Ahmed (MSc)
(05/13 - 12/13)
[LTC-HEV]

Guangchen Xiong

Dennis Xiong (MSc)
(05/13 - 05/14)
[LTC Engines]

Guangchen Xiong

Anup. Ketkale (MSc)
(09/13 - 04/14)
[LTC Engines]

Ajinkya Gitapathi

Ajinkya Gitapathi (MSc)
(01/14 - 04/14)
[LTC Engines]

Zhe Huang

Zhe Huang (MSc)
(11/13 - 8/14)
[HEV]

Tori Kovach

Tori Kovach (BSc)
(5/14 - 8/14)
[Building]

Ninad Ghike

Ninad Ghike (MSc)
(11/13 - 4/14)
[HEV]

Merwyn Cheruvathur

M. Cheruvathur (MSc)
(5/14 - 12/14)
[HEV]

Raviteja Reddy Zakkam

Raviteja Zakkam (MSc)
(1/14 - 10/14)
[LTC Engine]

Harshith Nutulapati

H. Nutulapati (MSc)
(5/14 - 10/14)
[LTC Engine]

Shivaram  Viswanathan

Sh. Viswanathan (MSc)
(11/13 - 4/14)
[HEV]

Yue Cao

Yue Cao (MSc)
(7/14 - 1/15)
[HEV]

Ebaad R. Malik

Ebaad R. Malik (MSc)
(10/15 - 04/16)
[HEV]

Vishal Ghadge

Vishal Ghadge (MSc)
(10/15 - 04/16)
[HEV]

Jayesh Dwivedi

Jayesh Dwivedi (MSc)
(10/15 - 6/16)
[HEV]

Karankumar Chandrakant Dhankani

Dhaval B. Lodaya
(MSc; 5/16 - 8/16)
[HEV]

Dhanraj Dhanraj

Dhanraj Dhanraj (MSc)
(10/15 - 8/16)
[HEV & Elec. Motor]

Karankumar Chandrakant Dhankani

Karan Dhankani (MSc)
(7/16 - 12/16)
[LTC Engines]

Visiting Scholars

Mate Pčolka

Matej Pčolka
(Visiting PhD candidate from Czech Tech University in Prague, 1/13 - 5/13)
Co-hosted by Prof. Rush Robinett III
[Building Energy Controls]

Mate Pčolka

Seyfi Polat
(Visiting PhD candidate from Gazi University in Turkey, 1/14 - 3/15)
[LTC Engines]

Mahdi Baloo

Mahdi Baloo
(Visiting PhD candidate from Amir Kabir University of Tech in Iran, 9/14 - 1/15)
Co-hosted by Prof. Seong-Young Lee
[Combustion Diagnosis]

Kamran Poorghasemi

Kamran Poorghasemi
(Visiting PhD candidate from Sahand University of Tech in Iran, 2/15 - 8/15)
[LTC Combustion]

Pouyan Ahmadizadeh

Pouyan Ahmadizadeh
(Visiting PhD candidate from Elmo-Sanat University in Iran,
2/16 - 10/16 )
[HEV]

Mohamed Toub

Mohamed Toub
(Fulbright visiting PhD candidate from Mohammadia Univ. in Morocco,
9/16 - )
Co-hosted by Prof. Rush Robinett III
[Power Grid]

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M. Shahbakhti