Education

  • Ph.D. 2013 – (2018)

    Doctorate in Computer Engineering - GPA: 4.0

    EECS Department, Henry Samueli School of Engineering
    University of California at Irvine, CA

  • M.Sc.2013 – 2015

    Master in Electrical and Computer Engineering - GPA: 4.0

    EECS Department, Henry Samueli School of Engineering
    University of California at Irvine, CA

  • B.Sc.2009 – 2013

    Bachelor in Electrical Engineering - GPA: 3.74

    Electrical Engineering Department (Digital Systems)
    Sharif University of Technology, Tehran

Professional Work Experience

  • Present 2013

    Research Assistant

    AICPS Research Lab., University of California at Irvine, CA

  • 09/2017 06/2017

    Research Scientist Intern

    Energy Department, NEC Laboratories America, Inc., Cupertino, CA

  • 2013 2012

    Hardware Engineering Intern

    SUT Research Lab., Sharif University of Technology, Tehran

  • 09/2012 06/2012

    Software Engineering Intern

    SUT Research Lab., Sharif University of Technology, Tehran

Honors, Awards and Grants

  • 2018
    William R. Harburger Memorial Dissertation Fellowship
    William R. Harburger Memorial Dissertation Fellowship awarded for the best research progeress and thesis in University of California at Irvine. Made possible by a generous gift from the Estate of Richard Harburger, three dissertation fellowships were awarded in 2017-2018 in honor of his late son, William R. Harburger.
  • 2017
    ICCAD Best Paper Award Nomination
    Best paper award nomination from International Conference on Computer-Aided Design (ICCAD) in Irvine, CA, for the paper titled "ACQUA: Adaptive and Cooperative Quality-Aware Control for Automotive Cyber-Physical Systems".
  • 2016
    DATE Best Paper Award
    Best paper award nomination from Design, Automation and Test in Europe (DATE) in Dresden, Germany, for the paper titled "OTEM: Optimized Thermal and Energy Management for Hybrid Electrical Energy Storage in Electric Vehicles".
  • 2015
    DAC Best Paper Award
    Best paper award nomination from Design Automation Conference (DAC) in San Francisco, CA, for the paper titled "Battery Lifetime-Aware Automotive Climate Control for Electric Vehicles".
  • 2015
    Best TA of the Year Finalist
    Best TA of the year finalist according to the excellent feedback and evaluation of the students regarding my teaching, EECS Department, University of California at Irvine.
  • 2014
    Prestigious Henry Samueli Fellowship
    The Prestigious Henry Samueli fellowship was awarded for three months of summer from EECS Department, University of California at Irvine.
  • 2013 – 2014
    One-Year Full Scholarship (Fellowship)
    One-year fellowship for graduate study from EECS Department, University of California at Irvine.
  • 2013
    Exceptionally Talented Student Award
    Exceptionally talented student award to be exempted from graduate exam for Sharif University of Technology.
  • 2013
    Ranked 2nd in Class of 2013 Graduates
    Ranked 2nd among class of 2013 graduates Electrical Engineering Dept. (Digital Systems) from Sharif University of Technology.
  • 2009 – 2013
    Exceptionally Talented Student Fellowship
    Exceptionally talented student award to offer 4-ysear full scholarship with stipend for Sharif University of Technology.
  • 2009
    Ranked 37th in Iran National Exam
    Ranked 37th in Iran undergraduate-level national exam among 300,000 students

Research Projects

  • Data-Driven Modeling, Vulnerability Detection, and Recovering

    Jan 2017 – Present
    Irvine, CA

    • modeled battery behavior by applying machine learning to experimental data for different conditions
    • achieved lower error rate in anomaly detection by looking into the data from the physical/control process
    • predicted the state of the physical system for recovering out of an attack or vulnerability in control
    • developed using machine learning – Conditional Generative Adversarial Networks (CGAN) in TensorFlow
  • Learning Towards Battery Optimal Demand Charge Reduction

    Summer 2017
    Cupertino, CA

    • predicted/classified industrial and commercial electricity load using machine learning (GMM, kNN) in OpenCV
    • modeled battery life based on discharged energy, over usage, and annual average SoC, for cost estimation
    • implemented cascaded MILP problem using GLPK (C++) to reduce electricity demand charge (-13%) while improving the battery lifetime (+1.6 yrs) by optimizing the battery charge and discharge pattern
  • Weather Rainfall Prediction

    Fall 2016
    Irvine, CA

    • developed a reconfigurable machine learning framework in python using scikit-learn
    • implemented and integrated various predictors by weighted stacking to predict rainfall given current weather
    • analyzed model performance using cross validation and ROC curve on real weather rainfall data (kaggle)
  • Driving Behavior Learning for EV Optimization

    Spring – Fall 2016
    Irvine, CA

    • gathered and processed real driving data from multiple drivers in different route conditions
    • developed a NARX-based machine learning model to predict driving behavior, forecast, and optimize EV states
  • Adaptive and Cooperative Quality-Aware Control

    Feb 2016 – Sep 2016
    Irvine, CA

    • developed runtime adaptive machine learning model (regression) to learn system behavior and optimize control
  • Lithium-ion Battery Modeling and Management System

    Jul 2015 – Present
    Irvine, CA

    • monitor/control real-time battery cells’ current, voltage, power, and temperature (python script)
    • emulated real-time EV power while driving real routes and standard driving cycles (NEDC, ECE, etc.)
    • implemented MATLAB control systems for EV to improve driving range and battery lifetime
  • Navigation System and Driving Management for EV

    Jul 2015 – Oct 2015
    Irvine, CA

    • collected real data of geographic map, route, traffic conditions using Google Maps APIs in JavaScript
    • implemented routing algorithms such as Dijkstra, Bellman-Ford, Floyd–Warshall, A* in MATLAB
    • formulated an MILP problem in MATLAB to optimize future EV consumption, charging, and driving route
  • Automotive Climate Control for EV

    Sep 2014 – Dec 2015
    Irvine, CA

    • created data-driven model for dynamic behavior of EV electric motor power, HVAC thermal behavior, HVAC power, and battery lifetime in MATLAB/Simulink and AMESim (Siemens)
    • developed MPC non-linear optimization algorithm in MATLAB for EV state prediction, energy optimization, and HVAC management to increase driving range and battery lifetime
  • Hybrid Energy Storage Management for EV

    Jan 2015 – Sep 2015
    Irvine, CA

    • created data-driven model for dynamic behavior of EV electric motor power, hybrid energy storage – Ultracapacitor, battery thermal behavior, and battery lifetime in MATLAB/Simulink and AMESim (Siemens)
    • developed MPC non-linear optimization algorithm in MATLAB for EV state prediction, energy optimization, and battery thermal management to increase driving range and battery lifetime
  • Energy Management in IoT using Fog Computing

    Jan 2015 – Sep 2015
    Irvine, CA

    • developed low cost, scalable energy management framework using IoT fog computing platform
    • implemented service-oriented architecture using C++, Python, UDP, and WSDL-gSOAP for device interactions
    • developed device-level and main control panel interface using JavaScript, AJAX, HTML, CSS
  • Drone Control Dynamics Modeling

    Fall 2013
    Irvine, CA

    • created detailed model of servo system and flight control of drone and simulated it using modelica
  • Real-Time Symbol Detector of OFDM Signal

    Spring 2012
    Tehran, Iran

    • developed real-time and fixed-point cross-correlation on FPGA/ASIC using Verilog HDL to estimate and detect Orthogonal Frequency-Division Multiplexing (OFDM) starting symbol

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Battery Optimal Approach to Demand Charge Reduction in Behind-The-Meter Energy Management Systems

K. Vatanparvar, R. Sharma
Conference Papers IEEE Power & Energy Society General Meeting (PES GM), 2018

Abstract

Large monthly demand charge of commercial and industrial entities is a major problem for their economical business. Utilizing a battery by behind-the-meter Energy Management Systems (EMS) has been seen as a solution to demand charge reduction. In state-of-the-art approaches, the EMS maintains sufficient energy for the unexpected large demands and uses the battery to meet them. However, large amount of energy stored in the battery may increase the average battery Stateof-Charge (SoC) and cause degradation in battery capacity. Therefore, the current approaches of demand charge reduction significantly shortens the battery lifetime which is not economical. In this paper, we propose a novel battery optimal approach to reduce the monthly demand charges. In our approach, load profile of the previous month is used by daily optimizations to shave daily power demands while considering the battery lifetime model. Evaluated daily demand thresholds and load profile are statistically analyzed to cluster different types of day. Hence, it helps the EMS to find the typical daily load profile and appropriate monthly demand threshold for the entity. The performance of our approach has been analyzed and compared to the state-of-the-arts by experimenting on multiple real-life load profiles and battery configurations. The results show significant reduction of 16% in annual average battery SoC that increases the battery lifetime from 4.1 to 5.6 years while achieving up to 13.4% demand charge reduction.

ACQUA: Adaptive and Cooperative Quality-Aware Control for Automotive Cyber-Physical Systems

K. Vatanparvar, M. A. Al Faruque
Conference Papers ACM/IEEE International Conference on Computer-Aided Design (ICCAD), 2017 (BEST PAPER AWARD NOMINATION)

Abstract

Controllers in cyber-physical systems integrate a designtime behavioral model of the system under design to improve their own quality. In the state-of-the-art control designs, behavioral models of other interacting neighbor systems are also integrated to form a centralized behavioral model and to enable a system-level optimization and control. Although this ideal embedded control design may result in pareto-optimal solutions, it is not scalable to larger number of systems. Moreover, the behavior of the multi-domain physical systems may be too complex for a control designer to model and may dynamically change at run time. In this paper, we propose a novel Adaptive and Cooperative Quality-Aware (ACQUA) control design which addresses these challenges. In this control design, an ACQUA-based controller for the system under design will monitor the quality of the neighbor systems to dynamically learn their behavior. Therefore, it can quickly adapt its control to cooperate with other neighbor controllers for improving the quality of not only itself, but also other neighbor systems. We apply ACQUA to design a cooperative controller for automotive navigation system, motor control unit, and battery management system in an electric vehicle. We use this automotive example to analyze the performance of the design. We show that by using our ACQUA control, we can reach up to 86% improvements achievable by an ideal embedded control design such that energy consumption reduces by 18% and battery capacity loss decreases by 12% compared to the state-of-the-art on average.

Driving Behavior Modeling and Estimation for Battery Optimization in Electric Vehicles

K. Vatanparvar, S. Faezi, I. Burago, M. Levorato, M. A. Al Faruque
Conference Papers ACM/IEEE International Conference on Hardware/Software Codesign and System Synthesis (CODES/ISSS) (ESWEEK), 2017

Abstract

Battery and energy management methodologies such as automotive climate controls have been proposed to address the design challenges of driving range and battery lifetime in Electric Vehicles (EV). However, driving behavior estimation is a major factor neglected in these methodologies. In this paper, we propose a novel context-aware methodology for estimating the driving behavior in terms of future vehicle speeds that will be integrated into the EV battery optimization. We implement a driving behavior model using a variation of Artificial Neural Networks (ANN) called Nonlinear AutoRegressive model with eXogenous inputs (NARX). We train our novel context-aware NARX model based on historical behavior of real drivers, their recent driving reactions, and the route average speed retrieved from Google Maps in order to enable driver-specific and self-adaptive driving behavior modeling and long-term estimation. Our methodology shows only 12% error for up to 30-second speed prediction which is improved by 27% compared to the state-of-theart. Hence, it can achieve up to 82% of the maximum energy saving and battery lifetime improvement possible by the ideal methodology where the future vehicle speed is known.

Eco-Friendly Automotive Climate Control and Navigation System for Electric Vehicles

K. Vatanparvar, M. A. Al Faruque
Conference Papers ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS), 2016

Abstract

Tight integration of cyber-dominated control systems and physical systems has helped Electric Vehicles (EV) as a Cyber-Physical System (CPS) to achieve zero-emission transportation capability. However, it poses major difficulties such as poor driving range, high price, and troublesome recharging which demotivate their consumers. These problems have arisen due to the stringent constraints on the EV battery packs. Moreover, the battery capacity degrades overtime and this degradation defines the battery lifetime and shortens the driving range further. The driving range and battery lifetime are mainly influenced by the route behavior, electric motor, and Heating, Ventilation, and Air Conditioning (HVAC) power consumption. In this paper, we present a novel jointly optimized eco-friendly automotive climate control and navigation system methodology. The integration between these two systems helps us to optimize the HVAC utilization and route for better battery lifetime and driving range. We have compared the performance of our methodology with the state-of-the-arts for different weather and route behavior. We have seen upto 24% improvement in battery lifetime and 17% reduction in energy consumption.

OTEM: Optimized Thermal and Energy Management for Hybrid Electrical Energy Storage in Electric Vehicles

K. Vatanparvar, M. A. Al Faruque
Conference Papers ACM/IEEE Design Automation & Test in Europe (DATE), 2016 (BEST PAPER AWARD)

Abstract

Electric Vehicles (EV) pose challenges in terms of reliability and performance which are due to the stringent design constraints. For instance, an insufficient energy storage restricts the EV driving range. Highly dense battery packs providing EV with the required power, may generate extreme internal heat which causes the battery temperature to rise significantly and thereby results in reliability and safety issues. Moreover, both high battery utilization and temperature may degrade the battery capacity and Battery LifeTime (BLT), which should be extended as much as possible to postpone expensive battery replacement costs. Although, researchers have provided separate battery energy and thermal managements for EVs to address the above-mentioned challenges, in this paper, we are bringing a joint optimized solution. Hence, we introduce a novel metric Thermal and Energy Budget (TEB) in a Hybrid Electrical Energy Storage (HEES) with an active battery cooling system. Furthermore, we propose a novel Optimized Thermal and Energy Management (OTEM) methodology which optimizes the battery/ultracapacitor utilization, battery temperature, and thereby TEB, in order to improve the driving range, extend the BLT, and maintain the battery temperature in the safe zone. Our methodology provides significant improvement in BLT (on average 16.8%) and average energy consumption (on average 12.1% reduction) compared to the state-of-the-art methodologies.

Modeling, analysis, and optimization of Electric Vehicle HVAC systems

M. A. Al Faruque, K. Vatanparvar
Conference Papers ACM/IEEE Asia and South Pacific Design Automation Conference (ASP-DAC), 2016

Abstract

Major challenges of driving range and battery lifetime in Electric Vehicles (EV) have been addressed by designing more efficient power electronics, advanced embedded hardware, and sophisticated embedded software. Besides the electric motor in EVs, Heating, Ventilation, and Air Conditioning (HVAC) has been seen as a significant contributor to the EV power consumption. The main responsibility of automotive climate controls has been to control the HVAC system in order to maintain the passengers' thermal comfort. However, the HVAC power consumption and its dynamic behavior may influence the battery lifetime and driving range significantly. Therefore, modeling and analyzing the HVAC system and its thermodynamic behavior may benefit the control designers to integrate the HVAC control and optimization into Battery Management Systems (BMS) for better battery lifetime and driving range. In this paper, the EV architecture, HVAC system dynamic behavior, and battery characteristics are explained and modeled. Automotive climate controls (e.g. battery lifetime-aware automotive climate control) and the benefits gained by system modeling and estimation for different conditions in terms of battery lifetime and driving range are illustrated. Moreover, present and future challenges regarding the HVAC system and control design are explained.

Battery-Aware Energy-Optimal Electric Vehicle Driving Management

K. Vatanparvar, J. Wan, M. A. Al Faruque
Conference Papers ACM/IEEE International Symposium on Low Power Electronics and Design (ISLPED), 2015

Abstract

Recently, Electric Vehicles (EVs) have been considered as new paradigm of transportation in order to solve environmental concerns, e.g. air pollution. However, EVs pose new challenges regarding their Battery LifeTime (BLT), energy consumption, and energy costs related to battery charging. The EV power consumption may be estimated by having the route information and the EV specifications. Also, by having the battery characteristics, the battery capacity consumption and the BLT may be estimated for each route. In this paper, we propose a driving management which uses the above-mentioned information in order to optimize the driving route by being aware of the EV energy consumption, energy cost, and BLT. Our proposed driving management extends the BLT by 16.8% and reduces the energy consumption by 11.9% and energy cost by 12.6% on average, by selecting the optimized route instead of the fastest route.

Battery Lifetime-Aware Automotive Climate Control for Electric Vehicles

K. Vatanparvar, M. A. Al Faruque
Conference Papers ACM/IEEE Design Automation Conference (DAC), 2015 (BEST PAPER AWARD)

Abstract

Electric Vehicle (EV) optimization involves stringent constraints on driving range and battery lifetime. Sophisticated embedded systems and huge number of computing resources have enabled researchers to implement advanced Battery Management Systems (BMS) for optimizing the driving range and battery lifetime. However, the Heating, Ventilation, and Air Conditioning (HVAC) control and BMS have not been considered together in this optimization. This paper presents a novel automotive climate control methodology that manages the HVAC power consumption to improve the battery lifetime and driving range. Our experiments demonstrate that the HVAC consumption is considerable and flexible in an EV which significantly influences the driving range and battery lifetime. Hence, this influence on the above-mentioned constraints has been modeled and analyzed precisely, then it has been considered thoroughly in the EV optimization process. Our methodology provides significant improvement in battery lifetime (on average 14%) and average power consumption (on average 39% reduction) compared to the state-of-the-art methodologies.

Demo abstract: Energy Management as a Service over Fog Computing Platform

K. Vatanparvar, M. A. Al Faruque
Conference Papers ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS), 2015

Abstract

Cyber-Physical Energy System (CPES) has been seen as the new paradigm of tight integration of power systems, embedded systems, control, and communication. CPES is capable of improving power grid reliability, efficiency, and performance by managing the supply and demand functionalities of the power systems effectively and intelligently. In this demo, we present an energy management system prototype for home and microgrid levels (both from residential domain), implemented over a fog computing platform. The prototype is capable of supporting interoperability, scalability, ease of deployment, cost effectiveness, open architecture, plug-n-play, and local and remote monitoring in a single package to fulfill the mandates by US Department of Energy (DOE).

Home Energy Management as a Service over Networking Platforms

K. Vatanparvar, Q. Chau, M. A. Al Faruque
Conference Papers IEEE PES Conference on Innovative Smart Grid Technologies (ISGT), 2015

Abstract

About 40% of the U.S. primary energy is consumed in the buildings. Therefore, to manage the energy consumption and its usages pattern, for instance, the U.S. state of California mandated Zero Net Energy buildings by 2020 for the residential sector (22% of the U.S. primary energy). Moreover, towards this goal, U.S. Department of Energy, Building Technologies Office is trying to develop techniques to improve the efficiency of buildings. Home Energy Management (HEM) may be used to improve the energy consumption in the residential buildings. However, the cost, scalability, and flexibility of the software and hardware architectures available, have increased the time-to-market and have made HEMs hard to penetrate the consumer market. To solve these limitations, in this paper, we have presented a novel HEM platform, which utilizes a low power IEEE 802.15.4 standard to monitor and control all the appliances in a house. Moreover, it utilizes Service-Oriented Architecture and Devices Profile for Web Services to implement the monitoring and controlling algorithms and therefore may significantly improve the scalability and flexibility of the platform. Also, some other features like: plug-n-play capability, remote access feature, and open source architecture are implemented in this platform. Finally, a table-top prototype of the HEM platform is demonstrated in the paper.

Control-as-a-Service in Cyber-Physical Energy Systems over Fog Computing

K. Vatanparvar, M. A. Al Faruque
Book ChapterFog Computing in the Internet of Things (Intelligence at the Edge), Springer, 2017
Cyber-Physical Energy Systems (CPES) are introduced for different levels (e.g., microgrid and home) of residential, commercial, and industrial domains. Different management methodologies are implemented using Internet-of-Things (IoT) to address their challenges. IoT has enabled the required interconnectivity for the devices in these systems. However, the management methodologies require multitude of different types of sensing and actuating devices which generate and process large sensed data. Hence, the complexity, scalability, heterogeneity, and performance of the methodologies become challenging with the growing number of these various devices. Implementing the control and management, e.g., energy management-as-a-service for these systems has been seen as a solution providing the required scalability, interactivity, and customizability of architecture. On the other hand, fog computing brings the computation (intelligence) close to the networking platform (edge) benefiting us to address interactivity, scalability, complexity, and inherent heterogeneity challenges. Proximity of the computation to the devices may improve the performance and dependability for delay-sensitive devices. Hence, fog computing has been seen as a promising platform for implementing control-as-a-service (CaaS) in the IoT and CPES. Herein, as an example, we illustrate an energy management-as-a-service implemented on a fog computing platform for a residential CPES.

Design and Analysis of Battery-Aware Automotive Climate Control for Electric Vehicles

K. Vatanparvar, M. A. Al Faruque
Journal PaperACM Transactions on Embedded Computing Systems (TECS), 2018 (under final revision)

Abstract

.. under final revision ..

Extended Range Electric Vehicle with Driving Behavior Estimation in Energy Management

K. Vatanparvar, S. Faezi, I. Burago, M. Levorato, M. A. Al Faruque
Journal PaperIEEE Transactions on Smart Grid (TSG), 2018

Abstract

Battery and energy management methodologies have been proposed to address the design challenges of driving range and battery lifetime in electric vehicles (EVs). However, the driving behavior is a major factor which has been neglected in these methodologies. In this paper, we propose a novel context-aware methodology to estimate the driving behavior in terms of future vehicle speeds and integrate this capability into EV energy management. We implement a driving behavior model using a variation of artificial neural networks called nonlinear autoregressive model with eXogenous inputs (NARX). We train our novel context-aware NARX model based on historical behavior of real drivers, their recent driving reactions, and route average speed retrieved from Google Maps in order to enable driver-specific and self-adaptive driving behavior modeling and long-term estimation. We analyze the estimation error of our methodology and its impact on a battery lifetime-aware automotive climate control, comparing to the state-of-the-art methodologies for various estimation window sizes. Our methodology shows only 12% error for up to 30-s speed prediction which is an improvement of 27% compared to the state-of-the-art. Therefore, the higher accuracy helps the controller to achieve up to 82% of the maximum energy saving and battery lifetime improvement achievable in ideal methodology where the future vehicle speeds are known.

Electric Vehicle Optimized Charge and Drive Management

K. Vatanparvar, M. A. Al Faruque
Journal PaperACM Transactions on Design Automation of Electronic Systems (TODAES), 2017

Abstract

Electric vehicles (EVs) have been considered as a solution to the environmental issues caused by transportation, such as air pollution and greenhouse gas emission. However, limited energy capacity, scarce EV supercharging stations, and long recharging time have brought anxiety to drivers who use EVs as their main mean of transportation. Furthermore, EV owners need to deal with a huge battery replacement cost when the battery capacity degrades. Yet in-house EV chargers affect the pattern of the power grid load, which is not favorable to the utilities. The driving route, departure/arrival time of daily trips, and electricity price influence the EV energy consumption, battery lifetime, electricity cost, and EV charger load on the power grid. The EV driving range and battery lifetime issues have been addressed by battery management systems and route optimization methodologies. However, in this article, we are proposing an optimized charge and drive management (OCDM) methodology that selects the optimal driving route, schedules daily trips, and optimizes the EV charging process while considering the driver’s timing preference. Our methodology will improve the EV driving range, extend the battery lifetime, reduce the recharging cost, and diminish the influence of EV chargers on the power grid. The performance of our methodology compared to the state of the art have been analyzed by experimenting on three benchmark EVs and three drivers. Our methodology has decreased EV energy consumption by 27%, improved the battery lifetime by 24.8%, reduced the electricity cost by 35%, and diminished the power grid peak load by 17% while increasing less than 20 minutes of daily driving time. Moreover, the scalability of our OCDM methodology for different parameters (e.g., time resolution and multiday cycles) in terms of execution time and memory usage has been analyzed.

Path to Eco-Driving: Electric Vehicle HVAC and Route Joint Optimization

K. Vatanparvar, M. A. Al Faruque
Journal PaperIEEE Design & Test, 2017

Abstract

Vehicle electrification and Battery Electric Vehicles (BEV) primarily attempt to alleviate the issues of air pollution and fossil fuel consumption. Major challenges of driving range and battery lifetime with BEVs, are addressed by controllers managing propulsion and non-propulsion powers. However, we believe the controllers can benefit from exploiting the relationships between these powers to harvest more energy towards eco-driving path. Hence, we propose a methodology to jointly optimize the Heating, Ventilation, and Air Condition system with driving route while considering electric motor. Our analysis shows that the methodology can increase driving range and battery lifetime up to 13% and 30% based on the driving condition.

A Security Perspective on the Battery Systems of the Internet of Things

A. Lopez, K. Vatanparvar, A. Prasad, S. Yang, S. Bhunia, M. A. Al Faruque
Journal PaperJournal of Hardware and Systems Security, Springer, 2017

Abstract

Battery (sub)systems are used in many systems (systems-of-systems) in the Internet of Things (IoT) ranging from everyday ones (e.g., mobile systems, home appliances, etc.) to safety-critical and/or mission-critical ones (e.g., electrical vehicles, unmanned aerial vehicles, autonomous underwater vehicles, etc.). As these systems become more interconnected with each other and their environments and batteries become more energy dense, the safety risks of using batteries increase. To guarantee effectiveness and prevent potential safety threats (i.e., failure, overheating, explosion), it is not only crucial to ensure that batteries are functioning correctly (via safety circuits and battery management system), but to also prevent security threats that specifically target the battery system from different parts of these systems. A security analysis is necessary for system manufacturers and users to understand what threats and solutions exist for battery system security. In this paper, we present a security perspective on battery systems, where we use a layered approach to analyze vulnerabilities, threats, and potential effects. We divide the battery system into the Physical, Battery Management System, and Application layers and use mobile systems and cyber-physical systems as case studies for IoT applications. We then highlight and discuss some existing solutions and mention the potential research directions on battery system security.

Compartmentalization-Based Design Automation Method for Power Grid

K. Vatanparvar, S Fakhouri, M. A. Siddika, M. A. Al Faruque
Journal PaperIET Cyber-Physical Systems: Theory & Applications, 2017

Abstract

Power grid design and maintenance are conducted to solve the problems caused by load growth over time and to stay within the constraints of voltage drop, power factor, etc. Typically, solutions to these problems are optimised individually. Considering multiple problems simultaneously and applying different solutions require vast design space exploration. This exclusively needs advanced algorithms and complex global optimisation methods which are not easily-applicable in different scenarios. In the state-of-the-art methods, for solving multiple problems simultaneously, these individually optimised solutions are applied sequentially to the power grid. In this so-called uncoordinated method, the final solution may not be optimal solution considering all the variables, since it is considering the overlapping effect of the solutions on the power grid. To validate the compartmentalisation method, a detailed distribution grid has been modeled. After analysing the possible solutions and optimisation, power loss was reduced 45% and total cost decreased by 71%, compared to the uncoordinated method.

Application-Specific Residential Microgrid Design Methodology

K. Vatanparvar, M. A. Al Faruque
Journal PaperACM Transactions on Design Automation of Electronic Systems (TODAES), 2016

Abstract

In power systems, the traditional, non-interactive, and manually controlled power grid has been transformed to a cyber-dominated smart grid. This cyber-physical integration has provided the smart grid with communication, monitoring, computation, and controlling capabilities to improve its reliability, energy efficiency, and flexibility. A microgrid is a localized and semi-autonomous group of smart energy systems that utilizes the above-mentioned capabilities to drive modern technologies such as electric vehicle charging, home energy management, and smart appliances. Design, upgrading, test, and verification of these microgrids can get too complicated to handle manually. The complexity is due to the wide range of solutions and components that are intended to address the microgrid problems. This article presents a novel Model-Based Design (MBD) methodology to model, co-simulate, design, and optimize microgrid and its multi-level controllers. This methodology helps in the design, optimization, and validation of a microgrid for a specific application. The application rules, requirements, and design-time constraints are met in the designed/optimized microgrid while the implementation cost is minimized. Based on our novel methodology, a design automation, co-simulation, and analysis tool, called GridMAT, is implemented. Our experiments have illustrated that implementing a hierarchical controller reduces the average power consumption by 8% and shifts the peak load for cost saving. Moreover, optimizing the microgrid design using our MBD methodology considering smart controllers has decreased the total implementation cost. Compared to the conventional methodology, the cost decreases by 14% and compared to the MBD methodology where smart controllers are not considered, it decreases by 5%.

Energy Management-as-a-Service Over Fog Computing Platform

M. A. Al Faruque, K. Vatanparvar
Journal PaperIEEE Internet of Things Journal (IoT), 2016

Abstract

By introducing microgrids, energy management is required to control the power generation and consumption for residential, industrial, and commercial domains, e.g., in residential microgrids and homes. Energy management may also help us to reach zero net energy (ZNE) for the residential domain. Improvement in technology, cost, and feature size has enabled devices everywhere, to be connected and interactive, as it is called Internet of Things (IoT). The increasing complexity and data, due to the growing number of devices like sensors and actuators, require powerful computing resources, which may be provided by cloud computing. However, scalability has become the potential issue in cloud computing. In this paper, fog computing is introduced as a novel platform for energy management. The scalability, adaptability, and open source software/hardware featured in the proposed platform enable the user to implement the energy management with the customized control-as-services, while minimizing the implementation cost and time-to-market. To demonstrate the energy management-as-a-service over fog computing platform in different domains, two prototypes of home energy management (HEM) and microgrid-level energy management have been implemented and experimented.

Design Space Exploration for the Profitability of a Rule-Based Aggregator Business Model Within Residential Microgrid

K. Vatanparvar, M. A. Al Faruque
Journal PaperIEEE Transactions on Smart Grid (TSG), 2014

Abstract

The microgrid has been shown to be profitable, reliable, and efficient for military, commercial, and university-like installations. However, until now, there has been no study to show how and when a residential microgrid may be profitable. Therefore, in this paper, we present a design space exploration methodology of the microgrid by modeling all the energy resources at the residential level and conducting numerous simulations with various parameters. Moreover, a set of rules are defined to make the stakeholders in the microgrid profitable. Also, by analyzing the number of houses in the microgrid, we observe that the number of years it takes to return the capital costs invested in the microgrid may become adequately short for a certain range of the number of houses. For instance, if the aggregator owns the renewable energy resources, e.g., solar panels, it may profit in less than five years when 500 houses participate in the microgrid where each house owns 500 sf solar panels. On the other hand, if the prosumers own the renewable energy resources, e.g., solar panels, the aggregator may profit in about a year. Typically, for an apartment-block type housing area in U.S. there are more than 1000 houses, therefore the aggregator profitability may improve furthermore.

Currrent Teaching

  • 2017 Winter

    Teaching Assistant and Lecturer Advanced System Software (EECS 211), University of California, Irvine

    Supervised students and organized Operating Systems homework, lectures, projects (nachos), and exams

  • 2017 Jan-Jun

    Undergraduate Student Mentor/Advisor, University of California, Irvine

    Developed and automated battery management and data gathering process using python scripts

Teaching History

  • 2016 Summer

    International Visitor Undergraduate Student Mentor/Advisor, University of California, Irvine

    Implemented battery management system, hardware, and control algorithms

  • 2016 Jan-Jun

    International Visitor Undergraduate Student Mentor/Advisor, University of California, Irvine

    Established and developed battery test bed, e.g. power supply, battery cells, variable load, sensors

  • 2015 Summer

    K-12 Student Mentor/Advisor, University of California, Irvine

    Organized and charted battery test bed equipment

  • 2015 Spring

    Teaching Assistant and Lecturer System Software (EECS 111), University of California, Irvine

    Supervised students and organized Operating Systems homework, lectures, projects (nachos), and exams

  • 2015 Winter

    Teaching Assistant and Lecturer Advanced System Software (EECS 211), University of California, Irvine

    Supervised students and organized Operating Systems homework, lectures, projects (nachos), and exams

  • 2015 2014

    Undergraduate Student Mentor/Advisor, University of California, Irvine

    Developed IoT framework of control-as-a-service for home and microgrid energy management

  • 2014 Summer

    K-12 Student Mentor/Advisor, University of California, Irvine

    Constructed a real miniature demo of smart house with energy management and multiple sensors

  • 2013 2012

    Teaching Assistant Microprocessor-based System Design, Sharif University of Technology

    Organized projects and exams. Developed graphical games using ARM, x86 emulators for teaching purpose

  • 2013 2012

    Laboratory Assistant Logic Circuit Design, Sharif University of Technology

    Created a manual/tutorial for the laboratory course and an educational CD for the course

  • 2012 2011

    Laboratory Assistant Logic Circuit Design, Sharif University of Technology

    Organized conduction of experiments using Logic Devices, Configurable Devices (PLD, FPGA)

  • 2012 Spring

    Webpage Designer Principles of Electronic and Ethics of Engineering, Sharif University of Technology

    Designed HTML/CSS webpages where students could download, upload files, and get the recent news

Invited Presentations

  • 2017

    ACQUA: Adaptive and Cooperative Quality-Aware Control for Automotive Cyber-Physical Systems
    ACM/IEEE International Conference on Computer-Aided Design (ICCAD)
    Irvine, CA, USA (BEST PAPER AWARD NOMINATION)

  • 2017

    Driving Behavior Modeling and Estimation for Battery Optimization in Electric Vehicles
    International Conference on Hardware/Software Codesign and System Synthesis (CODES/ISSS) (ESWEEK)
    Seoul, South Korea

  • 2016

    Reliable and Energy Efficient Battery-Powered Cyber-Physical Systems
    Cyber Physical System EECS 227, University of California, Irvine (UCI)
    Irvine, CA, USA

  • 2015

    Battery Lifetime-Aware Automotive Climate Control for Electric Vehicles
    ACM/IEEE Design Automation Conference (DAC)
    San Francisco, CA, USA (BEST PAPER AWARD)

  • 2015

    Demo Abstract: Energy Management as a Service over Fog Computing Platform
    ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS)
    Seattle, WA, USA

Technical Program Committee

  • 2018

    Special Session on Design of Cyber-Physical Systems, Design of Cyber-Physical Systems (DCPS), Prague, Czech Republic

Reviewing Committee

  • 2018

    IEEE Power & Energy Society General Meeting (PES GM)

  • 2018

    International Conference on Cyber-Physical Systems (ICCPS)

  • 2018

    Design Automation & Test in Europe (DATE)

  • 2017

    KSII Transactions on Internet and Information Systems

  • 2017

    International Conference on Hardware/Software CoDesign and System Synthesis (CODES+ISSS)

  • 2017

    International Conference on Cyber-Physical Systems (ICCPS)

  • 2017

    Design Automation & Test in Europe (DATE)

  • 2017

    International Conference on Computer-Aided Design (ICCAD)

  • 2016

    Elsevier Vehicular Communications

  • 2016

    IEEE Transactions on Power Systems

  • 2016

    International Conference on Cyber-Physical Systems (ICCPS)

  • 2016

    International Conference on Computer-Aided Design (ICCAD)

  • 2016

    Design Automation Conference (DAC)

  • 2015

    IEEE Transactions on Power Systems

  • 2015

    International Conference on Computer-Aided Design (ICCAD)

At My Lab

5440 Engineering Hall
University of California, Irvine
Irvine, CA 92697-2625
USA