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Brushless DC (BLDC) motor is the first choice for lightweight electric vehicles because of its high torque, high power density, and compatible speed range. The vehicle environment is very dynamic, nonlinear, and noisy. It is challenging to design a BLDC motor control for high-performance operation. Therefore this paper presents Nonlinear Model Predictive Control (NMPC) with online state estimation techniques for speed and torque control. We investigate the performance of three estimation techniques integrated with an NMPC strategy. The estimation techniques include Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Nonlinear Moving Horizon Estimation (NMHE). The comparative study is performed where the state variables are estimated from noisy measurements of output variables. Results of the closed-loop NMPC performance with three estimation techniques are presented and analyzed with different performance indicators. The results show the integration of NMHE with NMPC provides better performance than other estimation techniques. However, the NMHE is computationally expensive as compared to EKF and UKF.

In the electric three-wheeler vehicular segment, permanent magnet (PM) motors are strong competitors for traction applications. The vehicular environment faces sudden load variations, where traditional control like field-oriented control (FOC) over-excites the machine. It also degrades the machine’s overall performance due to dynamic changes in the motor states with limited bandwidth of the cascaded proportional-integral (PI) controllers; this makes the overall motor operation inefficient. The paper proposes a model predictive control (MPC) based on torque ripple and over-current excitation minimization for permanent magnet motors to overcome these challenges. The proposed controller is tailored to improve the overall electrical efficiency of permanent magnet motors while considering the real back EMF. The performance of the proposed controller is tested in simulation, considering an electric three-wheeler as a load along with a European driving cycle. The feasibility of the proposed method is tested by deployment on (1) STM32F546ZG micro-controller and (2) Xilinx’s ZYNQ-7000 SoC ZC706 FPGA and validated the implementation results with hardware-in-the-loop(HIL) co-simulation. The implementation results show that the proposed MPC outperforms the conventionally used FOC in energy consumption, torque ripples minimization, and torque disturbance handling while tracking the desired speed accurately with load variations.

Hybrid systems can be controlled by a Hybrid Model Predictive Control (HMPC) with an accurate prediction model and proper formulation. In the real-time evolution of HMPC, an online optimization (mostly, Mixed Integer Quadratic Programming (MIQP)) problem needs to be solved at each sample time. Online implementation of MIQP solver on resource-limited embedded platforms poses challenges, which limits its use for the slow dynamic systems. The online burden of MIQP solver can be moved offline by multi-parametric programming. In this paper, we present an implementation of explicit HMPC on Field Programmable Gate Array (FPGA) for the control of Mixed Logical Dynamical (MLD) systems. We show the step-by-step procedure of FPGA implementation of HMPC considering the model of the inverted pendulum. In the implementation, we used MATLAB-based Hybrid Toolbox to construct hybrid MPC and subsequently generate a low-level C/C++ code of the HMPC. Further, the generated C/C++ code is optimized for memory, speed, and resource utilization by the customized approach of applying pipelining and directives using Xilinx Vivado. The customized explicit HMPC is then implemented on a Xilinx ZYNQ-7000 SoC ZC706 FPGA. The implemented approach is compared with the linear (online and explicit) MPC with a linearized model of the system. The detailed analysis of controller computational complexity in terms of memory, resource utilization, clock, and power consumption is presented. The performance of implemented HMPC is verified through Hardware-in-the-Loop (HIL) co-simulations.

Embedded implementation of real-time Nonlinear Model Predictive Control (NMPC) is extremely challenging and complex. This paper presents a framework for implementation of NMPC on Field Programmable Gate Array (FPGA). We show the step-by-step procedure of FPGA implementation framework design of NMPC for a case study of 2D-crane system. In the implementation, we used GRAMPC software to construct NMPC and subsequently generate an FPGA specific low-level C/C++ code of the optimization solver. Generated C/C++ code is optimized for memory, speed, and resource utilization by the customized approach of applying pipelining and directives using Xilinx Vivado HLS toolchain. The NMPC is implemented on a Xilinx’s ZYNQ-7000 SoC ZC706 FPGA board. The detailed analysis of the controller computational complexity in terms of memory, resource utilization, clock, and power consumption is presented. The performance of implemented NMPC is verified through Hardware-in-the-Loop (HIL) co-simulation using system generator tool. The presented results show the feasibility of FPGA-based GRAMPC framework for ultra-fast applications of NMPC.

In this paper, we present the offset-free Nonlinear Model Predictive Controller (NMPC) for the control of drum-boiler pilot-plant at College of Engineering Pune (COEP), India. We consider a laboratory-scale pilot plant available in the process control lab designed to generate steam up to 0.0083 kg/s at the pressure 0.4 MPa. To design an NMPC, we developed a Multi-Input Multi-Output (MIMO) nonlinear model of the pilot plant. The developed model is based on the first principle (mass and energy balance) equations where the thermodynamic properties and its partial differentiation values for the desired operating region are calculated by IAPWS-IF97 and transformation rule. The developed model is validated through several experiments on the pilot plant and the same is used as a prediction model for NMPC. Further, we designed an Extended Kalman Filter (EKF) for offset-free NMPC (disturbance modeling and state estimation) to handle any disturbances in the output measurements. The designed offset-free NMPC strategy is then implemented for the nonlinear MIMO model of the boiler to control the desired pressure by regulating three inputs. Closed-loop performance of the offset-free NMPC is evaluated and compared with NMPC without state estimation. Presented results show that the designed offset-free NMPC performs well and successfully handle disturbances in the output measurements.

This work propose the sliding mode controller (SMC)-based field oriented control (FOC) for brushless direct current (BLDC) motor, and in later part investigated the speed and position estimation using higher order sliding mode observer (HOSMO), which may come as a solution for sensorless operation. The main idea of this work is to improve the dynamic response and reduce torque ripples in the BLDC motor with the proposed techniques. Speed tracking performance has been analyzed with different statistical performance and torque ripples with percentage variation with respect to the applied external torque. Further, rotor position and speed estimation using HOSMO is validated against real-time measured values and checked for percentage error. To validate the results of proposed approach, the SMC with HOSMO is implemented on a STM32Nucleo-144 development board and its performance is analyzed by hardware-in-the-loop co-simulations for reference tracking. Results shows that the designed SMC is a promising candidate for the BLDC motor control, with characteristics of improved speed tracking response for a wide speed range with minimum values for error performance indices and torque ripples are reduced by 5.84%.

This paper proposes a framework for the high-accuracy, low-precision, and memory-efficient embedded model predictive control (MPC) using the posit™ numbers and its implementation on the ARM-based embedded platform. A quadratic programming (QP) problem in posit-based linear MPC is solved by the active set method (ASM) with a Cholesky factorization-based linear solver. The main idea of this paper is to encode all data associated with the QP problem as posit numbers and employ posit number arithmetic to synthesis the ASM algorithm. We provide a detailed analysis of a posit number that acts as a memory-efficient replacement of the IEEE 754 floating-point standard numbers. We show the posit-based ASM algorithm employed in MPC and its implementation on STM32 Nucleo-144 development board with STM32F746ZG MCU. The results of hardware-in-loop (HIL) simulations with the detailed analysis of memory utilization and performance of the posit-based ASM algorithm is shown with two case studies. HIL results show that the proposed approach can reduce memory footprints by 50% to 75% without losing control accuracy and performance.

Model predictive control (MPC) has emerged as an excellent control strategy owing to its ability to include constraints in the control optimization and robustness to linear as well as highly non-linear systems. There are many challenges in real-time implementation of MPC on embedded devices, including computational complexity, numerical instability, and memory constraints. Advances in machine learning-based approaches have widened the scope to replace the traditional and intractable optimization algorithms with advanced algorithms. In this paper, a novel deep learning-based model predictive control (DNN-MPC) is presented. The proposed MPC uses recurrent neural network (RNN) to accurately predict the future output states based on the previous training data. Using deep neural networks for the real-time embedded implementation of MPC, on-line optimization is completely eliminated leaving only the evaluation of some linear equations. Closed-loop performance evaluation of the DNN-MPC is verified through hardware-in-loop (HIL) co-simulation on ARM microcontroller and a 4x speed-up in computational time for a single iteration is achieved over the conventional MPC. Detailed analysis of DNNMPC complexity (speed and memory requirement) is presented and compared with traditional MPC. Results show that the proposed DNN-MPC performs faster and with less memory footprints while retaining the controller performance.

The maximum distance that can be traveled at a stretch is the major limitation of today’s electric vehicle (EV). This is due to the need for maximum current, torque, and, the total onboard energy storage, etc. The distance can be increased by efficiently using the available power resources. In this paper, we present a nonlinear model predictive control (NMPC) scheme for the control of brushless direct current (BLDC) motor in EV. A control-oriented nonlinear model of the BLDC motor with EV load is considered and used in the proposed NMPC scheme. The objective of the NMPC is to control the desired torque and speed of the motor by minimizing the energy with constraints on supplied current and maximum speed. The simulation results of BLDC motor control with EV and fixed mechanical load are presented. Further, the performance of NMPC is compared with the conventional direct torque control (DTC) scheme. Presented results show that NMPC improves energy savings by 19% in fixed-load and 13% with EV load as compared to DTC under provided conditions of speed reference.

For explicit model predictive control (EMPC), off-line pre-computed optimal feedback laws need to be stored in a look-up table for on-line evaluation. The need for memory to store the look-up table on embedded hardware limits applicability of EMPC to systems with few states, a small number of constraints, and short prediction horizons. In this paper, we present a novel technique to reduce the memory footprints of EMPC solutions. The idea is based on encoding all data (i.e., the critical regions and the feedback laws) as positTM numbers, which can be viewed as a memory-efficient replacement for the IEEE 754 floating-point standard. By using the posit number system, we achieve more accuracy with fewer bits, and posits can be efficiently deployed on embedded hardware like PLC, FPGA, DSP, ARM, etc. We show the design and implementation of posit-based EMPC for the control of the coupled tank system. Results show that the total memory footprints can be reduced by 75% without losing control accuracy. An additional advantage of the approach is that it can be applied on the top of existing complexity reduction techniques.

Today, various nonlinear programming problem (NLP) solvers and C/C++ code generation frameworks are available as open source for solving nonlinear model predictive control (NMPC). Almost all the solvers are written in C/C++ code which are more compatible for the PC-based simulation environment. These codes are not directly compatible for embedded implementation and real-time control. An attempt has been made to address this shortcoming by creating a customized framework on top of the C code generated from ACADO Toolkit to make it directly compatible with ARM based embedded platforms. The study also analyzes the embedded implementation aspects using C code generated for qpOASES, qpDUNES, and HPMPC solvers from ACADO Toolkit. We show the results of hardware-in-loop (HIL) simulations with detailed analysis and comparison of memory requirement and achievable sampling time for three benchmark dynamical systems on different embedded platforms viz ARM Cortex M3, PYNQ FPGA and Raspberry Pi. The results show that qpOASES outperforms as compared to the other two solvers when the computational time is of prime importance for small prediction horizon. Similarly, when there are limited on-chip memory resources, qpDUNES can prove beneficial.

With increasingly fast-paced life and alarmingly high rate of chronic ailments in general population, there is a need for quickening the current process of healthcare monitoring, especially in emergency situations. The recent developments in communication technology, especially in the field of Internet of Things (IoT) have enhanced the accessibility of such systems. This can be achieved by transmitting/uploading the data of various health parameters acquired by different physiological sensors with wireless sensor networks onto the cloud platform. This data later can be accessed by the concerned medical authorities when required for diagnosis. In this work, we focus on the development of the scalable IoT framework for monitoring the physiological parameters of the patient. The customized MATLAB-based GUI is designed to perform real-time analysis of sensor’s data which is used for continuous monitoring of vital parameters of the body. We have developed a wearable band which can be worn as a wristband by the patient. The band consists of temperature, pulse, and ECG sensors those are used to transmit the vital parameters of the patient integrated with the ultra-low-power battery-operated Texas Instruments MSP430 microcontroller with CC110L sub-1 GHz RF wireless transceiver. The concept is successfully demonstrated by transmitting three physiological parameters wirelessly over 100 m distance as well as over the cloud platform.

It is well-known that the real-time implementation of Model Predictive Control (MPC) is cumbersome because of the huge burden of solving optimization problem on-line at each sample time. Due to this, MPC has been mainly restricted to processes with rather slow dynamics, such as the ones encountered in the oil and gas refineries. However, recent algorithmic advances (such as the explicit MPC) allowed usage of MPC to problems arising in the automotive or power electronics industry where the time scales are in the milli- to microsecond range. This paper focuses on the Field Programmable Gate Array (FPGA) implementation of offset-free explicit MPC and its detailed analysis for the position control of PMDC motor. We show the analysis of controller computational complexity in terms of memory, resource utilization, clock and power consumption. Along that effects of various tuning parameters on the number of regions is also presented with respect to the changing prediction horizon length. Finally, the performance of implemented offset-free explicit MPC is compared with the standard explicit MPC and Proportional-Integral controller for reference-tracking, constraints handling, and disturbance rejection. Results indicate that the performance of offset-free explicit MPC is superior but at the cost of increased memory footprint.

This paper deals with the education of Instrumentation and Control Engineering students in the field of process control. A laboratory scale drum-boiler pilot plant is utilized to serve the teaching purpose. The emphasis of the paper is to give hands-on experience in designing and implementing widely used controllers in many process industries, such as Proportional-Integral (PI) and Proportional-Integral-Derivative (PID) controllers for the drum-boiler plant. Such educative hands-on experiences allow a student to better understand the practical aspects of the process control. Drum-boiler plant is widely found in several process industries to generate required pressurized steam. We developed a MATLAB/Simulink-based human plant interface to read/write data to the plant. A steam flow control loop is controlled by designed PI and PID controllers with an anti-windup scheme. The experimental results of steam flow rate tracking are shown using different anti-windup schemes. Developed directives and control schemes can be used for effective process control education of instrumentation and control engineers.

A lithium-plating side reaction at the lithiated graphite (LiC6) anode leads to poor safety of the lithium-ion battery. Faster charging at normal temperature may lead to a plating side reaction during the end of charging at the anode-separator interface. At lower temperature, the lithium-plating side reaction may become thermodynamically favorable during almost the entire charging period, even at low rates. This paper uses an electrochemical engineering model and dynamic optimization framework to derive charging profiles to minimize lithium plating at low temperatures. Transport parameters for lithium-ion battery are very sensitive at low temperatures. This paper shows the derivation of the optimal charging profile considering strict lower bounds on the plating reaction depending on various thermal insulation conditions (adiabatic, isothermal, and normal heat transfer coefficient) surrounding the battery.

Electrical drives play an important role in the implementation of tasks for increasing productivity in various industries, for automation and complex mechanization of production processes. In this paper three strategies; a classical Proportional-Integral-Derivative (PID), a Linear Quadratic Regulator (LQR) and a Model Predictive Control (MPC) are designed and implemented for the DC motor speed control. All these strategies are compared on the basis of output response, steady state error, controller efforts, and constraints handling for the reference tracking. Performed simulations shows that though the controller efforts are less in PID and LQR but MPC outperforms for reference tracking and constraints handling.

In this paper, Second Derivative Method (SDM) of numerical discretization is applied to optimal control problems. Convergence rates for the error between the discretized solution of SDM and the corresponding analytical solution of optimal control problems are analyzed. Illustrative examples are included to demonstrate the applicability and benefits of SDM. The comparison of the convergence rates of SDM with implicit Runge-Kutta methods (third order, 2-stage RadauIIA and fourth order, 3-stage LobattoIIIA) is also presented. Using SDM, for optimal control problems with non-stiff type of state equations, the fourth order convergence for states and second order convergence for controls is observed, while for certain stiff/oscillatory equations, it results in reduced order of convergence as observed in other approaches. Depending on the choice of optimization algorithms/platforms used, the proposed method is found to be comparable to other approaches and for certain cases, more efficient.

The polymer electrolyte membrane fuel cell (PEMFC) has emerged as a promising fuel cell technology for a varied range of stationary as well as transportation applications. This paper presents a dynamic model of a PEMFC system which can be used for further development of a control system for the same. The proposed model incorporates various dynamic operating conditions like changes in load current, pressures of input reactant gases and the cell operating temperature. The model has been implemented in MATLAB as well as a simulation has been verified in LabVIEW. The simulation has been designed to be very user-friendly and conveniently alter the various dynamic conditions and observe the responses. For the purpose of validating the model, a comparison has been made by using experimental data from the PEM fuel cell testing. The presented dynamic model can thus be useful in testing various control strategies and designing a controller for the fuel cell system.

This paper presents the design and real-time hardware implementation of Active Set Method (ASM) based Linear Model Predictive Controller (MPC) for position control of DC Servomotor. A key component of MPC is to solve an online convex Quadratic Programming (QP) optimization problem at each sampling instance within specified sample period. In view of ASM, Cholesky factorization based linear solver is used to solve the KKT system, which renders faster execution of MPC. The designed approach is demonstrated for real-time MPC implementation on Quanser QET DC Servomotor setup.

The proposed system focuses on Remote Monitoring of patients to ensure the availability of quality medical services. The system comprises of Electrocardiogram (ECG) electrodes, SpO2 probes, signal conditioning units, micro controller unit, and a GSM Module. The weak signals obtained from the sensors are amplified, filtered, current to voltage converted and analog to digital converted. Finally the data is sent to the doctor's phone via GSM.

In this paper we have designed a simple, robust and energy efficient street light control system which requires minimum maintenance. The principle used is sensing time for deciding the intensity of the street light. The astronomical clock depending on geographical area is studied to generate statistical data. The intensity is varied in five steps as per the variations in ambient natural light obtained from the statistical data. The system uses micro controller, real time clock and MOSFET based driver circuit for controlling intensity of an LED array. Energy saving is 15.96 KWhr per street light per year in comparison with simple on-off systems. The system is more reliable than sensor based systems and requires less maintenance as compared to wireless sensor networks.

Ziegler Nichols is oldest and widely accepted PID tuning method. Due to excessive overshoot in Ziegler-Nichol tuned PIDs (ZNPID), their performance is usually not acceptable for applications where small error tolerance band and precise control is required. To overcome this problem, we propose the gain updating method called as Augmented Ziegler-Nichols PID (AZNPID). We proposed an algorithm, which uses fourth order Runge-Kutta method to solve differential equations involved in PID, which eventually improves the peak overshoot of AZNPID compared to ZNPID. The proposed AZNPID is tested for third order and fourth order linear processes and its performance is compared with ZNPID.

This paper focuses on the design and implementation of model- predictive controller (MPC) for the close loop control of anesthesia for a patient undergoing surgery. A single input (Propofol infusion rate) single output (bispectral index (BIS)) model of patient has been assumed which includes variable dead time caused by measurement of bispectral index. The main motivation for the use of MPC in this case relies on its ability in considering, in a straightforward way, control and state constraints that naturally arise in practical problems. The MPC can take into account constraints on drug delivery rates and state of the patient but requires solving an optimization problem at regular time intervals. We proposed a modified active set method algorithm using Cholesky factorization based linear solver to solve online convex quadratic programming (QP) problem, to reduce complexity of the algorithm, eventually to accelerate MPC implementation. Experimentation shows that excellent regulation of bispectral index (BIS) is achieved around set-point targets.

Linear model predictive control (MPC) assumes a linear system model, linear inequality constraints and a convex quadratic cost function. Thus, it can be formulated as a quadratic programming (QP) problem. Due to associated computational complexity of QP solving algorithms, its applicability is restricted to relatively slow dynamic systems. This paper presents an interior-point method (IPM) based QP solver for the solution of optimal control problem in MPC. We propose LU factorization to solve the system of linear equations efficiently at each iteration of IPM, which renders faster execution of MPC. The approach is demonstrated practically by applying MPC to QET DC Servomotor for position control application.

A lithium-ion battery loses its capacity with cycle life.1 This capacity loss or fade is due to various mechanisms which are largely due to unwanted side reactions, taking place in these batteries. Some of these mechanisms include the loss of active material in SEI layer growth, loss of lithium in plating side reactions, irreversible phase change in solid particles, electrolyte decomposition at high temperatures, along with mechanical degradation or breaking of particles due to intercalation induced stresses.2,3 The contribution of each of those factors depends on the cell chemistry and the operating conditions. The collective effects of these individual fade mechanisms are still being investigated. Some of these fade mechanisms are potentially unsafe to the batteries, for eg. the plating side reaction could lead to dendrite formation which could internally short the battery. This motivates the need to be able to manage and use the batteries more efficiently in order to enhance the cycle life of the batteries. Physics-based model predictive control (MPC) is one of the ways to achieve that.4-6 In our past work,7-8 using dynamic optimization strategies, we have developed the optimal charging protocols to minimize the capacity fade due to SEI-layer formation, due to lithium-plating and intercalation-induced stresses, while controlling internal temperatures inside the batteries, using physics-based reformulated 1D and 2D models.9-10 In order to experimentally show and quantify the benefits of the MPC approach, the tests were conducted at NREL on an actual 16Ah NMC based prismatic Kokam cell. The SEI-layer based fade mechanism was chosen as it reasonably mimicked the actual fade of the cells. The optimal profiles based on minimizing SEI layer formation was chosen and the cells were cycled using those profiles. Fig. 1 shows the experimental results for capacity of the battery vs cycle life for a CC-CV charge/5C discharge compared to MPC charge/5C discharge. We have shown that using such an approach could lead to as much as 20% increase in the usable capacity of the batteries or an increase in cycle life by up to 40%. Acknowledgements The work presented herein was funded in part by the Advanced Research Projects Agency – Energy (ARPA-E), U.S. Department of Energy, under Award Number DE-AR0000275 along with the Clean Energy Institute (CEI) at the University of Washington (UW) and the Washington Research Foundation. References J. Newman, K. E. Thomas, H. Hafezi, and D. R. Wheeler, J. Electrochem. Soc., 150, A176 (2003). P. Arora, R.E.White, and M. Doyle, Electrochem. Soc., 145(10), 3647 (1998). V. Ramadesigan, P. W. C. Northrop, S. De, S. Santhanagopalan, R. D. Braatz, and V. R. Subramanian, J. Electrochem. Soc., 159(3), R31 (2012). M. Doyle, T. F. Fuller and J. Newman, J. Electrochem. Soc., 140, 1526 (1993). T. F. Fuller, M. Doyle and J. Newman, J. Electrochem. Soc., 141, 1 (1994). M. D. Canon, C. D. Cullum, and E. Polak, Theory of Optimal Control and Mathematical Programming, McGraw-Hill, New York (1969). B. Suthar, V. Ramadesigan, S. De, R. D. Braatz and V. R. Subramanian, Phy.Chem.Chem. Phy., 16(1), 277 (2014). B. Suthar, P. W.C. Northrop, R. D. Braatz and V. R. Subramanian, J. Electrochem. Soc., 161(11), F3144 (2014). P. W. C. Northrop, V. Ramadesigan, S. De, and V. R. Subramanian, J. Electrochem. Soc., 158(12), A1461 (2011). P. W. C. Northrop, M. Pathak, D. Rife, S. De, S. Santhanagopalan and V. R. Subramanian, J. Electrochem. Soc, 162(6), A940 (2015). Figure 1

There is no way to directly measure the state of charge (SOC) for a Lithium-ion battery. Various methods for estimating the state of charge have been proposed like extended Kalman filtering, coulomb counting and impedance spectroscopy but these are not entirely reliable [1, 2]. The models need to be recalibrated after a few cycles in order for accurately predicting the state of charge. The prediction of SOC of individual cells is even more important for a stack of cells in order to efficiently use the stack. Differences in the SOC of individual cells in a stack could lead to the cells being charged/discharged to different potentials, which could affect the safety of the stack or could damage the cells [2, 3, 4]. Cell balancing therefore is an important functionality of any Battery Management System (BMS) which necessitates the accurate estimation of SOC. In this paper, we present the estimation of SOC of individual batteries in a stack using a physics based reformulated model [4], with the use of the voltage-time data while charging the cells. This kind of model adds more fidelity to the SOC estimation as the battery undergoes degradation after many cycles. Further, optimal charging profiles for the battery stacks could be suggested that would maximize the amount of charge stored while keeping the battery safe. Reformulated models enable efficient simulation and control for online control applications. Acknowledgements The work presented herein was funded in part by the Clean Energy Institute at the University of Washington, Seattle and the Advanced Research Projects Agency – Energy (ARPA-E), U.S. Department of Energy, under the Award Number DE-AR0000275. References V. Pop, H.J. Bergveld, J.H.G. Op het Veld, P.P.L. Regtien, D. Danilov, and P.H.L. Notten, J. Electrochem. Soc., 153 (11), A2013-A2022, 2006. W.B. Gu and C.Y. Wang, J. Electrochem. Soc., 147(8), 2910–2922, 2000. A. Szumanowski and Y. Chang, IEEE Trans. on Vehic. Tech., 57(3), 1425-1432, 2008. P. W. C. Northrop, V. Ramadesigan, S. De, and V. R. Subramanian, J. Electrochem. Soc., 158(12), A1461-A1477, 2011. F. Zhang, G. Liu and L. Fang, Advanced Intelligent Mechatronics, AIM, IEEE/ASME International Conference on , 1008-1013, 2008. J. Newman, Electrochemical Systems, 2nd ed. Prentice Hall, 1991. V. Ramadesigan, K. Chen, N.A. Burns, V. Boovaragavan, R. D. Braatz, and V. R. Subramanian, J. Electrochem. Soc., 158(9), A1048-A1054, 2011.

The use of infrared radiation for medical diagnosis is a very new concept in the field of medical technology that promises to deliver high-end results. While a very few devices based on infrared technique have been implemented, there still exists a strong need to develop such medical devices. Therefore, our aim is to develop a low-cost subcutaneous vein detection system, which uses infrared technique to capture vein images, processes (enhances contrast) these images, and displays them on screen. These images can also be projected on the limb. This type of system will provide an easy access to the doctor for efficient drug delivery. A vein imaging system has been implemented successfully using the ARM 9 single board computer as the development platform. A customized webcam is used for capturing vein images. Thereafter, contrast enhancement algorithms based on histogram equalization method have been tested, implemented and the experimental results are presented in this paper.

The internet has grown rapidly and has a more impact on the education sector than it had before. This paper aims at describing a novel way of extending the e-Learning techniques used to the area of embedded Linux education. For these techniques, Open Source Software (OSS) technologies [viz. AJAX, PHP, Apache] have been used in an innovative way to impart embedded Linux education just by use of normal web-browsers which make the learning system as easy as checking a mail. In this paper, ways of enabling the embedded device's Linux shell access through a web-browser is explained, which is a key feature of the system. The novelty in the idea lies in its scalability and accessibility for multiple users.

The paper presents a novel methodology to implement resource efficient 64-bit floating point matrix multiplication algorithm using FPGA. Approach uses systolic architecture using four processing element (PE's) that gives tradeoffs between resource utilization and execution time, results in reducing the routing complexity for dense matrix multiplication problems.

The paper presents a systolic architecture for integer point matrix multiplication algorithm using FPGA. Approach uses four processing elements that minimizes resources, reduces the routing complexity and improves Area/Speed metric.

Progress made in renewable energy technologies (e.g. solar, wind and tidal energy) has fueled the research in energy storage technologies which are indispensible for the success of these intermittent sources of energy. Electro-chemical energy storage systems specially redox flow batteries (RFB) offer modular and scalable energy storage systems for large scale energy storage1. Research in different RFBs has been pursued to improve the efficiency, cost and safety aspects of these systems. While research on most aspects requires small scale (lab-scale) redox flow battery, research on some aspects can be performed and analyzed using a static cell set up (e.g. cross contamination, characterization of membrane and electrolyte etc.). These static cells (H cell) can be used to quickly estimate the performance of redox flow batteries (at least at the lab scale) including capacity loss due to cross contaminations2. Use of static cell with simplified modeling approach (including capacity loss) to validate experimental data is demonstrated by Tang et. al.2 on vanadium redox flow battery. This approach can be made more robust and general to be useful for any redox couple by combining electrochemical models and robust optimization framework (parameter and state estimation techniques) to increase the confidence in predictions made by static cell. This presentation will focus on electrochemical models for static cell with parameters and state estimation framework to increase the fidelity of results obtained by experiments performed. Acknowledgements The authors acknowledge financial support from the U.S. Department of Energy’s Advanced Research Projects Agency- Energy (ARPA-E), and the Solar Energy Research Institute in India and the United States (SERIIUS), as well as, Washington University in St. Louis’ McDonnell Academy Global Energy and Environmental Partnership (MAGEEP) and SunEdison Grant. References 1. B. R. Chalamala, T. Soundappan, G. R. Fisher, M. R. Anstey, V. V. Viswanathan and M. L. Perry, Proceedings of the IEEE, 102, 976 (2014). 2. A. Tang, J. Bao and M. Skyllas-Kazacos, J. Power Sources, 196, 10737 (2011).

Accurate estimation of battery internal states i.e. state of charge (SOC), state of health (SOH) and battery power is of great significance for safe and optimal performance of proactive Battery Management Systems (BMS) (1). Mathematical models for lithium-ion batteries (2, 3) vary with complexity, computational costs and reliability of predictions. Incorporating more physicochemical phenomena in a model can improve its prediction accuracy but they cannot be directly employed for online control due to their high computational requirements. So, it is important to have reformulated battery models for online control and accurate predictions of internal states. Efforts have been made in past (4), to reformulate battery models to significantly reduce its computational expenses, allowing for real time control and simulation. In this work, we explored the use of an experimentally validated reformulated pseudo two dimensional model to derive the optimal charging profiles at different temperatures. The two distinct dynamic optimization strategies i.e. simultaneous optimization (5) and control vector parameterization (CVP) (6, 7, 8) to estimate optimal charging current profile for effective use of lithium-ion batteries are evaluated. Acknowledgements The work presented herein was funded in part by the Advanced Research Projects Agency – Energy (ARPA-E), U.S. Department of Energy, under Award Number DE-AR0000275. References V. Pop, H. J. Bergveld, D. Danilov, P. P. L. Regtien and P. H. L. Notten, Battery management systems: accurate state-of-charge indication for battery powered applications, Springer Verlag (2008). M. Doyle, T. F. Fuller and J. Newman, J. Electrochem. Soc.,140, 1526 (1993). T. F. Fuller, M. Doyle and J. Newman, J. Electrochem. Soc., 141, 1 (1994). P. W. C. Northrop, V. Ramadesigan, S. De and V. R. Subramanian, J. Electrochem. Soc., 158, A1461 (2011). Biegler, L.T. Cervantes, A. M. Wachter, A., Advances in simultaneous strategies for dynamic process optimization, Chemical Engineering Science, 57(4):575-593, (2002). J. R. Banga, E. Balsa-Canto, E. G. Moles, and A. A. Alonso, Dynamic optimization of bioprocesses: Efficient and robust numerical strategies. Journal of Biotechnology, 117:407–419, (2005). R. N. Methekar, V. Ramadesigan, R. D. Braatz, and V. R. Subramanian, Optimal charging profile for lithium-ion batteries to maximize energy storage and utilization, ECS Trans, vol. 25, no. 35, pp. 139-146, (2010). Fathy, Hosam K., Reyer, Julie A., Papalambros Panos Y., and Ulsoy, A. Galip, On the Coupling between the Plant and Controller Optimization Problems, in Proceedings of the ACS, (2001).

Optimal performance of a proactive Battery Management System (BMS) needs real time knowledge of internal states of the battery such as Sate-of-Charge (SOC), State-of-Health (SOH) and battery power [1]. The effect of inaccurate state estimations may cause severe damage to the battery. The physics based battery models that could predict the internal states are inherently computationally challenging. This is difficult for implementation in dedicated microprocessors or microcontrollers with limited on-chip memory and low CPU clock. Efforts have been made in the past to come up with different low complexity and reformulated models which are capable of being implemented in microcontrollers for BMS operation [2-4]. In this work, we propose reformulated physics-based electrochemical models which are highly computationally efficient to deploy in low cost microcontrollers (like 32-bit AVR or Beagle Bone) for accurate state estimation of batteries. This talk will particularly focus on numerical simulation techniques that enable real-time simulation and optimization in microcontroller environment. Acknowledgements The authors acknowledge financial support provided by the US Department of Energy’s Advanced Research Projects Agency-Energy (ARPA-E), under award number DE-AR0000275. References [1] V. Pop, Battery Management Systems: Accurate State-of-Charge Indication for Battery Powered Applications, Dordrecht, Springer (2008). [2] M. Farkhondeh, and C. Delacourt, Mathematical Modeling of Commercial LiFePO4 Electrodes Based on Variable Solid-State Diffusivity, J. Electrochem Soc. 159, A177 (2012). [3] R. Klein, N. A. Chaturvedi, J. Christensen, J. Ahmed, R. Findeisen and A. Kojic, in Proceedings of the American Control Conference, p. 6618 (2010). [4] P. W. C. Northrop, V. Ramadesigan, S. De, and V. R. Subramanian, “Coordinate Transformation, Orthogonal Collocation and Model Reformulation for Simulating Electrochemical-Thermal Behavior of Lithium-ion Battery Stacks,” J. Electrochem. Soc. 158(12), A1461-A1477 (2011).