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Dr. Sohel Anwar
Mechatronics and Automotive Research Lab, Department of Mechanical and Energy Engineering, School of Engineering and Technology, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, USA

Basic Info


Research Keywords & Expertise

0 Novel sensor development and data fusion
0 Advanced diagnostics/management of traction batteries
0 Power/Energy management of electrified powertrains
0 Autonomous vehicle control and X-by-wire systems
0 Biomechanical device design and control

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Short Biography

Dr. Sohel Anwar is a Professor of Mechanical and Energy Engineering at the Purdue School of Engineering and Technology, Indiana University Purdue University Indianapolis (IUPUI), Indiana, USA. He is also the director of the Mechatronics and Automotive Research Lab (MARL), IUPUI. He published over 150 papers in peer-reviewed journal and conference proceedings. He is also an inventor or co-inventor on 14 US patents.

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Journal article
Published: 15 August 2021 in Energies
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World wind energy output is steadily increasing in both production scale and capacity of harvesting wind. Hydrostatic transmission systems (HTSs) have been used mostly in offshore wind turbine applications. However, their potential has not been fully utilized in onshore wind turbines, partially due to concerns related to hydraulic losses. In our prior work, it was shown that the annual energy production from a hydrostatic wind turbine can match or exceed that of a mechanical drive wind turbine with appropriate optimal control techniques. In this paper, we present an optimal control technique that can further improve energy production of a hydrostatic wind turbine, particularly in low speed regions. Here, the overall loss equation of the HTS is developed and used as a cost function to be minimized with respect to system model dynamics. The overall loss function includes the losses due to both the aerodynamic efficiencies and the hydrostatic efficiencies of the motor and pump. A nonlinear model of HST is considered for the drive train. Optimal control law was derived by minimizing the overall loss. Both unconstrained and constrained optimization using Pontryagin’s minimum principle were utilized to derive two distinct control laws for the motor displacement. Simulation results showed that both the controllers were able to increase power output with the unconstrained optimization offering better results for the HTS wind turbine in the low speed regions (3–8 m/s).

ACS Style

Ammar E. Ali; Majid Deldar; Sohel Anwar. Optimal Control of Hydrostatic Drive Wind Turbines for Improved Power Output in Low Wind-Speed Regions. Energies 2021, 14, 5001 .

AMA Style

Ammar E. Ali, Majid Deldar, Sohel Anwar. Optimal Control of Hydrostatic Drive Wind Turbines for Improved Power Output in Low Wind-Speed Regions. Energies. 2021; 14 (16):5001.

Chicago/Turabian Style

Ammar E. Ali; Majid Deldar; Sohel Anwar. 2021. "Optimal Control of Hydrostatic Drive Wind Turbines for Improved Power Output in Low Wind-Speed Regions." Energies 14, no. 16: 5001.

Preprint content
Published: 16 July 2021
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ACS Style

Nazmuzzaman Khan; Sohel Anwar; Mohammad A Hasan; Veera P. Rajendran. Clustering Algorithm Based Straight and Curved Crop Row Detection Using Color Based Segmentation. 2021, 1 .

AMA Style

Nazmuzzaman Khan, Sohel Anwar, Mohammad A Hasan, Veera P. Rajendran. Clustering Algorithm Based Straight and Curved Crop Row Detection Using Color Based Segmentation. . 2021; ():1.

Chicago/Turabian Style

Nazmuzzaman Khan; Sohel Anwar; Mohammad A Hasan; Veera P. Rajendran. 2021. "Clustering Algorithm Based Straight and Curved Crop Row Detection Using Color Based Segmentation." , no. : 1.

Preprint content
Published: 16 July 2021
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ACS Style

Nazmuzzaman Khan; Veera P. Rajendran; Mohammad A Hasan; Sohel Anwar. Clustering Algorithm Based Straight and Curved Crop Row Detection Using Color Based Segmentation. 2021, 1 .

AMA Style

Nazmuzzaman Khan, Veera P. Rajendran, Mohammad A Hasan, Sohel Anwar. Clustering Algorithm Based Straight and Curved Crop Row Detection Using Color Based Segmentation. . 2021; ():1.

Chicago/Turabian Style

Nazmuzzaman Khan; Veera P. Rajendran; Mohammad A Hasan; Sohel Anwar. 2021. "Clustering Algorithm Based Straight and Curved Crop Row Detection Using Color Based Segmentation." , no. : 1.

Preprint content
Published: 16 July 2021
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ACS Style

Jonathan Bowyer; Sohel Anwar. Capacity Fade Estimation of a Lithium-Ion Battery Through an Integrated Electrochemical Battery Model and Empirical Cycle Aging Model. 2021, 1 .

AMA Style

Jonathan Bowyer, Sohel Anwar. Capacity Fade Estimation of a Lithium-Ion Battery Through an Integrated Electrochemical Battery Model and Empirical Cycle Aging Model. . 2021; ():1.

Chicago/Turabian Style

Jonathan Bowyer; Sohel Anwar. 2021. "Capacity Fade Estimation of a Lithium-Ion Battery Through an Integrated Electrochemical Battery Model and Empirical Cycle Aging Model." , no. : 1.

Journal article
Published: 06 January 2021 in IEEE Access
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Polymer nanocomposites (PNC) have a great potential for in-situ strain sensing applications in both static and dynamic loading scenarios. These PNCs, having a polymer matrix of polyvinylidene fluoride (PVDF) with a conductive filler of multi-walled carbon nanotubes (MWCNT), have both piezoelectric and piezoresistive characteristics. Generally, this composite would accurately measure either low frequency dynamic strain using piezoresistive characteristic or high frequency dynamic strains using piezoelectric characteristics of the MWCNT/PVDF film sensor. This limits the frequency bands of the strain sensor to either piezoresistive or piezoelectric ranges. In this study, a novel weighted fusion technique, called piezoresistive/piezoelectric fusion (PPF), is proposed to combine both piezoresistive and piezoelectric characteristics to capture wide frequency bands of strain measurements in real time. This fuzzy logic (FL) based method combines the salient features (i.e. piezoresistive and piezoelectric) of the nanocomposite sensor via reasonably accurate models to extend the frequency range over a wider band. The FL determines the weight of each signal based on the error between the estimate and actual measurements. These weights indicate the contribution of each signal to the final fused measurement. The fuzzy inference system (FIS) was developed using both optimization and data clustering techniques. In addition, type-2 FIS was utilized to overcome the model’s uncertainty limitations. The developed PPF methods were verified with experimental data at different dynamic frequencies that were obtained from existing literature. The fused measurements of the MWCNT/PVDF were found to correlate very well with the actual strain and a high degree of accuracy was achieved by the subtractive clustering PPF’s FISs algorithm.

ACS Style

Ahmed Alotaibi; Sohel Anwar. A Fuzzy Logic Based Piezoresistive/Piezoelectric Fusion Algorithm for Carbon Nanocomposite Wide Band Strain Sensor. IEEE Access 2021, 9, 14752 -14764.

AMA Style

Ahmed Alotaibi, Sohel Anwar. A Fuzzy Logic Based Piezoresistive/Piezoelectric Fusion Algorithm for Carbon Nanocomposite Wide Band Strain Sensor. IEEE Access. 2021; 9 (99):14752-14764.

Chicago/Turabian Style

Ahmed Alotaibi; Sohel Anwar. 2021. "A Fuzzy Logic Based Piezoresistive/Piezoelectric Fusion Algorithm for Carbon Nanocomposite Wide Band Strain Sensor." IEEE Access 9, no. 99: 14752-14764.

Journal article
Published: 09 October 2020 in Energies
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This research paper presents a look-ahead optimal control strategy for a Hydro-static Drive Wind Turbine when look ahead wind speed information is available. The proposed predictive controller is a direct numerical optimizer based on the well established principles of Hamilton-Jacobi-Bellman (Dynamic Programming). Hydro-static transmission based, non-linear model of wind turbine is used in this optimization work. The optimal behavior of the turbine used the non-linearity of aerodynamic maps and hydro-static drive train by a convex combination of state space controller with measurable generator speed and hydraulic motor displacement as scheduling parameters. A comparative analysis between a optimal controller based on Maximum Power Point Tracking (MPPT) algorithm as published in literature and the proposed look ahead based predictive controller is presented. The simulation results show that proposed look ahead strategy offered optimal operation of the wind turbine by closely tracking the optimal tip-speed ratio to maximize capacity factor while also maintaining the hydraulic motor speed close to the desired value to ensure that the frequency of electrical output is constant. It is observed from the simulation results that the proposed predictive controller provided around 3.5% better performance in terms of improving total system losses and harvesting energy as compared to the MPPT algorithm.

ACS Style

Sourav Pramanik; Sohel Anwar. Look Ahead Based Control Strategy for Hydro-Static Drive Wind Turbine Using Dynamic Programming. Energies 2020, 13, 5240 .

AMA Style

Sourav Pramanik, Sohel Anwar. Look Ahead Based Control Strategy for Hydro-Static Drive Wind Turbine Using Dynamic Programming. Energies. 2020; 13 (20):5240.

Chicago/Turabian Style

Sourav Pramanik; Sohel Anwar. 2020. "Look Ahead Based Control Strategy for Hydro-Static Drive Wind Turbine Using Dynamic Programming." Energies 13, no. 20: 5240.

Journal article
Published: 26 November 2019 in Sensors
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To apply data fusion in time-domain based on Dempster–Shafer (DS) combination rule, an 8-step algorithm with novel entropy function is proposed. The 8-step algorithm is applied to time-domain to achieve the sequential combination of time-domain data. Simulation results showed that this method is successful in capturing the changes (dynamic behavior) in time-domain object classification. This method also showed better anti-disturbing ability and transition property compared to other methods available in the literature. As an example, a convolution neural network (CNN) is trained to classify three different types of weeds. Precision and recall from confusion matrix of the CNN are used to update basic probability assignment (BPA) which captures the classification uncertainty. Real data of classified weeds from a single sensor is used test time-domain data fusion. The proposed method is successful in filtering noise (reduce sudden changes—smoother curves) and fusing conflicting information from the video feed. Performance of the algorithm can be adjusted between robustness and fast-response using a tuning parameter which is number of time-steps( t s ).

ACS Style

Nazmuzzaman Khan; Sohel Anwar. Time-Domain Data Fusion Using Weighted Evidence and Dempster–Shafer Combination Rule: Application in Object Classification. Sensors 2019, 19, 5187 .

AMA Style

Nazmuzzaman Khan, Sohel Anwar. Time-Domain Data Fusion Using Weighted Evidence and Dempster–Shafer Combination Rule: Application in Object Classification. Sensors. 2019; 19 (23):5187.

Chicago/Turabian Style

Nazmuzzaman Khan; Sohel Anwar. 2019. "Time-Domain Data Fusion Using Weighted Evidence and Dempster–Shafer Combination Rule: Application in Object Classification." Sensors 19, no. 23: 5187.

Journal article
Published: 05 November 2019 in Sensors
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Multi-sensor data fusion technology in an important tool in building decision-making applications. Modified Dempster–Shafer (DS) evidence theory can handle conflicting sensor inputs and can be applied without any prior information. As a result, DS-based information fusion is very popular in decision-making applications, but original DS theory produces counterintuitive results when combining highly conflicting evidences from multiple sensors. An effective algorithm offering fusion of highly conflicting information in spatial domain is not widely reported in the literature. In this paper, a successful fusion algorithm is proposed which addresses these limitations of the original Dempster–Shafer (DS) framework. A novel entropy function is proposed based on Shannon entropy, which is better at capturing uncertainties compared to Shannon and Deng entropy. An 8-step algorithm has been developed which can eliminate the inherent paradoxes of classical DS theory. Multiple examples are presented to show that the proposed method is effective in handling conflicting information in spatial domain. Simulation results showed that the proposed algorithm has competitive convergence rate and accuracy compared to other methods presented in the literature.

ACS Style

Nazmuzzaman Khan; Sohel Anwar. Paradox Elimination in Dempster–Shafer Combination Rule with Novel Entropy Function: Application in Decision-Level Multi-Sensor Fusion. Sensors 2019, 19, 4810 .

AMA Style

Nazmuzzaman Khan, Sohel Anwar. Paradox Elimination in Dempster–Shafer Combination Rule with Novel Entropy Function: Application in Decision-Level Multi-Sensor Fusion. Sensors. 2019; 19 (21):4810.

Chicago/Turabian Style

Nazmuzzaman Khan; Sohel Anwar. 2019. "Paradox Elimination in Dempster–Shafer Combination Rule with Novel Entropy Function: Application in Decision-Level Multi-Sensor Fusion." Sensors 19, no. 21: 4810.

Regular paper
Published: 15 January 2019 in Asian Journal of Control
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A hydrostatic drivetrain transmits wind turbine energy to a generator. One hydrostatic transmission system (HTS) configuration utilizes a fixed displacement pump and a variable displacement motor. The system dynamics are captured in a nonlinear multi‐input multi‐output mathematical model. This paper introduces a decentralized control configuration based on this model to achieve two desired objectives: maximizing the harvested energy without direct measurement of wind and regulating the frequency of the generator without using power electronic converters. To accomplish these objectives, suitable pairing of control actuators and system responses are identified through nonlinear relative gain arrays (RGA) analysis. The pairing also provides a strong decoupling of control loops. So maximum power point tracking (MPPT) is achieved independently while the generator speed is regulated to maintain the frequency of generated power at 60 Hz. Simulation results demonstrate robust performance of MPPT and frequency regulation in the presence of uncertainties in the turbine and HTS model. We also demonstrate that the RGA paired input‐out control configuration offers superior performance over other possible input–output paired control configurations.

ACS Style

M. Deldar; A. Izadian; S. Anwar. A decentralized multivariable controller for hydrostatic wind turbine drivetrain. Asian Journal of Control 2019, 22, 1038 -1051.

AMA Style

M. Deldar, A. Izadian, S. Anwar. A decentralized multivariable controller for hydrostatic wind turbine drivetrain. Asian Journal of Control. 2019; 22 (3):1038-1051.

Chicago/Turabian Style

M. Deldar; A. Izadian; S. Anwar. 2019. "A decentralized multivariable controller for hydrostatic wind turbine drivetrain." Asian Journal of Control 22, no. 3: 1038-1051.

Journal article
Published: 04 January 2019 in Batteries
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With the ever-increasing usage of lithium-ion batteries, especially in transportation applications, accurate estimation of battery state of charge (SOC) is of paramount importance. A majority of the current SOC estimation methods rely on data collected and calibrated offline, which could lead to inaccuracies in SOC estimation under different operating conditions or when the battery ages. This paper presents a novel real-time SOC estimation of a lithium-ion battery by applying the particle swarm optimization (PSO) method to a detailed electrochemical model of a single cell. This work also optimizes both the single-cell model and PSO algorithm so that the developed algorithm can run on an embedded hardware with reasonable utilization of central processing unit (CPU) and memory resources while estimating the SOC with reasonable accuracy. A modular single-cell electrochemical model, as well as the proposed constrained PSO-based SOC estimation algorithm, was developed in Simulink©, and its performance was theoretically verified in simulation. Experimental data were collected for healthy and aged Li-ion battery cells in order to validate the proposed algorithm. Both simulation and experimental results demonstrate that the developed algorithm is able to accurately estimate the battery SOC for 1C charge and 1C discharge operations for both healthy and aged cells.

ACS Style

Arun Chandra Shekar; Sohel Anwar. Real-Time State-of-Charge Estimation via Particle Swarm Optimization on a Lithium-Ion Electrochemical Cell Model. Batteries 2019, 5, 4 .

AMA Style

Arun Chandra Shekar, Sohel Anwar. Real-Time State-of-Charge Estimation via Particle Swarm Optimization on a Lithium-Ion Electrochemical Cell Model. Batteries. 2019; 5 (1):4.

Chicago/Turabian Style

Arun Chandra Shekar; Sohel Anwar. 2019. "Real-Time State-of-Charge Estimation via Particle Swarm Optimization on a Lithium-Ion Electrochemical Cell Model." Batteries 5, no. 1: 4.

Journal article
Published: 26 April 2018 in Journal of Engineering and Science in Medical Diagnostics and Therapy
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Instrument-assisted soft tissue manipulation (IASTM) is a form of manual therapy which is performed with rigid cast tools. The applied force during the IASTM process has not been quantified or regulated. Nor have the angle of treatment and strokes frequency been quantified which contribute to the overall recovery process. This paper presents a skin modeling analysis used in the design of a novel mechatronic device that measures force in an IASTM application with localized pressures, similar to traditional, nonmechatronic IASTM devices that are frequently used to treat soft tissue dysfunctions. Thus, quantifiable soft tissue manipulation (QSTM) represents an advancement in IASTM. The innovative mechatronic QSTM device is based on one-dimensional (1D) compression load cells, where only four compression force sensors are needed to quantify all force components in three-dimensional (3D) space. Here, such a novel QSTM mechatronics device is simulated, analyzed, and investigated using finite element analysis (FEA). A simplified human arm was modeled to investigate the relationship between the measured component forces, the applied force, and the stress and strain distribution on the skin surface to validate the capability of the QSTM instrument. The results show that the QSTM instrument as designed is able to correlate the measured force components to the applied tool-tip force in a straight movement on the skin model.

ACS Style

Ahmed Mohammed Al Otaibi; Sohel Anwar; M. Terry Loghmani. Skin Modeling Analysis of a Force Sensing Instrument-Assisted Soft Tissue Manipulation Device. Journal of Engineering and Science in Medical Diagnostics and Therapy 2018, 1, 031002 .

AMA Style

Ahmed Mohammed Al Otaibi, Sohel Anwar, M. Terry Loghmani. Skin Modeling Analysis of a Force Sensing Instrument-Assisted Soft Tissue Manipulation Device. Journal of Engineering and Science in Medical Diagnostics and Therapy. 2018; 1 (3):031002.

Chicago/Turabian Style

Ahmed Mohammed Al Otaibi; Sohel Anwar; M. Terry Loghmani. 2018. "Skin Modeling Analysis of a Force Sensing Instrument-Assisted Soft Tissue Manipulation Device." Journal of Engineering and Science in Medical Diagnostics and Therapy 1, no. 3: 031002.

Journal article
Published: 01 January 2018 in AIMS Energy
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ACS Style

Ammar E. Ali; Nicholas C. Libardi; Sohel Anwar; Afshin Izadian. Design of a compressed air energy storage system for hydrostatic wind turbines. AIMS Energy 2018, 6, 229 -244.

AMA Style

Ammar E. Ali, Nicholas C. Libardi, Sohel Anwar, Afshin Izadian. Design of a compressed air energy storage system for hydrostatic wind turbines. AIMS Energy. 2018; 6 (2):229-244.

Chicago/Turabian Style

Ammar E. Ali; Nicholas C. Libardi; Sohel Anwar; Afshin Izadian. 2018. "Design of a compressed air energy storage system for hydrostatic wind turbines." AIMS Energy 6, no. 2: 229-244.

Journal article
Published: 01 January 2018 in AIMS Energy
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ACS Style

Majid Deldar; Afshin Izadian; Sohel Anwar. Analysis of a hydrostatic drive wind turbine for improved annual energy production. AIMS Energy 2018, 6, 908 -925.

AMA Style

Majid Deldar, Afshin Izadian, Sohel Anwar. Analysis of a hydrostatic drive wind turbine for improved annual energy production. AIMS Energy. 2018; 6 (6):908-925.

Chicago/Turabian Style

Majid Deldar; Afshin Izadian; Sohel Anwar. 2018. "Analysis of a hydrostatic drive wind turbine for improved annual energy production." AIMS Energy 6, no. 6: 908-925.

Journal article
Published: 25 August 2017 in Energies
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Electrochemical model-based condition monitoring of a Li-Ion battery using an experimentally identified battery model and Hybrid Pulse Power Characterization (HPPC) cycle is presented in this paper. LiCoO2 cathode chemistry was chosen in this work due to its higher energy storage capabilities. Battery electrochemical model parameters are subject to change under severe or abusive operating conditions resulting in, for example, Navy over-discharged battery, 24 h over-discharged battery, and overcharged battery. Stated battery fault conditions can cause significant variations in a number of electrochemical battery model parameters from nominal values, and can be considered as separate models. Output error injection based partial differential algebraic equation (PDAE) observers have been used to generate the residual voltage signals in order to identify these abusive conditions. These residuals are then used in a Multiple Model Adaptive Estimation (MMAE) algorithm to detect the ongoing fault conditions of the battery. HPPC cycle simulated load profile based analysis shows that the proposed algorithm can detect and identify the stated fault conditions accurately using measured input current and terminal output voltage. The proposed model-based fault diagnosis can potentially improve the condition monitoring performance of a battery management system.

ACS Style

Ashiqur Rahman; Sohel Anwar; Afshin Izadian. Electrochemical Model-Based Condition Monitoring via Experimentally Identified Li-Ion Battery Model and HPPC. Energies 2017, 10, 1266 .

AMA Style

Ashiqur Rahman, Sohel Anwar, Afshin Izadian. Electrochemical Model-Based Condition Monitoring via Experimentally Identified Li-Ion Battery Model and HPPC. Energies. 2017; 10 (9):1266.

Chicago/Turabian Style

Ashiqur Rahman; Sohel Anwar; Afshin Izadian. 2017. "Electrochemical Model-Based Condition Monitoring via Experimentally Identified Li-Ion Battery Model and HPPC." Energies 10, no. 9: 1266.

Journal article
Published: 18 July 2017 in Journal of Medical Devices
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Instrument-assisted soft tissue manipulation (IASTM) is a form of mechanotherapy, e.g., massage, that uses rigid devices which may be machined or cast. The delivered force, which is a critical parameter during IASTM, is not measured and not standardized in current clinical IASTM practice. In addition to the force, the angle of treatment and stroke frequency play an important role during IASTM. For accurate IASTM treatment, there is a strong need to scientifically characterize the IASTM delivered force, angle of treatment, and stroke frequency. This paper presents a novel, mechatronic design of an IASTM device that can measure the localized pressure on the soft tissue in a clinical treatment. The proposed design uses a three-dimensional (3D) load cell, which can measure all three-dimensional force components simultaneously. The device design was implemented using an IMUduino microcontroller board which provides tool orientation angles. These orientation angles were used for coordinate transformation of the measured forces to the tool–skin interface. Additionally, the measured force value was used to compute the stroke frequency. This mechatronic IASTM tool was validated for force measurements in the direction of tool longitudinal axis using an electronic plate scale that provided the baseline force values to compare with the applied force values measured by the tool. The load cell measurements and the scale readings were found to agree within the expected degree of accuracy.

ACS Style

Ahmed M. Alotaibi; Sohel Anwar; M. Terry Loghmani; Stanley Chien. Force Sensing for an Instrument-Assisted Soft Tissue Manipulation Device. Journal of Medical Devices 2017, 11, 031012 .

AMA Style

Ahmed M. Alotaibi, Sohel Anwar, M. Terry Loghmani, Stanley Chien. Force Sensing for an Instrument-Assisted Soft Tissue Manipulation Device. Journal of Medical Devices. 2017; 11 (3):031012.

Chicago/Turabian Style

Ahmed M. Alotaibi; Sohel Anwar; M. Terry Loghmani; Stanley Chien. 2017. "Force Sensing for an Instrument-Assisted Soft Tissue Manipulation Device." Journal of Medical Devices 11, no. 3: 031012.

Book chapter
Published: 19 October 2016 in MEMS Product Development
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This work presents a novel approach to particulate material (soot) measurement in a diesel particulate filter (DPF) using electrical capacitance tomography (ECT). Modern diesel engines are equipped with DPFs, as well as onboard technologies to evaluate the status of DPF because complete knowledge of DPF soot loading is very critical for robust and efficient operation of the engine exhaust after treatment system. Emission regulations imposed upon all internal combustion engines including diesel engines on gaseous as well as particulate (soot) emissions by environment regulatory agencies. In course of time, soot will be deposited inside the DPFs which tend to clog the filter and hence generate a back pressure in the exhaust system, negatively impacting the fuel efficiency. To remove the soot buildup, regeneration of the DPF must be done as an engine exhaust after treatment process at predetermined time intervals. Passive regeneration increases the exhaust heat to burn the deposited soot while active regeneration injects external energy in, such as injection of diesel into an upstream diesel oxidation catalyst (DOC), to burn the soot. Since the regeneration process consumes fuel, a robust and efficient operation based on accurate knowledge of the particulate matter deposit (or soot load) becomes essential in order to keep the fuel consumption at a minimum. Here we propose a sensing method for a DPF that can accurately measure in-situ soot load using ECT. Lab experimental results show that the proposed method offers an effective way to accurately estimate the soot load in DPF. The proposed method is expected to have a profound impact in improving overall DPF efficiency (and thereby fuel efficiency), and durability of a DPF through appropriate closed loop regeneration operation.

ACS Style

Ragibul Huq; Sohel Anwar. Soot Load Sensing in a Diesel Particulate Filter Based on Electrical Capacitance Tomography. MEMS Product Development 2016, 217 -252.

AMA Style

Ragibul Huq, Sohel Anwar. Soot Load Sensing in a Diesel Particulate Filter Based on Electrical Capacitance Tomography. MEMS Product Development. 2016; ():217-252.

Chicago/Turabian Style

Ragibul Huq; Sohel Anwar. 2016. "Soot Load Sensing in a Diesel Particulate Filter Based on Electrical Capacitance Tomography." MEMS Product Development , no. : 217-252.

Journal article
Published: 29 September 2016 in Sensors
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This paper presents the design of an innovative device that applies dynamic mechanical load to human knee joints. Dynamic loading is employed by applying cyclic and periodic force on a target area. The repeated force loading was considered to be an effective modality for repair and rehabilitation of long bones that are subject to ailments like fractures, osteoporosis, osteoarthritis, etc. The proposed device design builds on the knowledge gained in previous animal and mechanical studies. It employs a modified slider-crank linkage mechanism actuated by a brushless Direct Current (DC) motor and provides uniform and cyclic force. The functionality of the device was simulated in a software environment and the structural integrity was analyzed using a finite element method for the prototype construction. The device is controlled by a microcontroller that is programmed to provide the desired loading force at a predetermined frequency and for a specific duration. The device was successfully tested in various experiments for its usability and full functionality. The results reveal that the device works according to the requirements of force magnitude and operational frequency. This device is considered ready to be used for a clinical study to examine whether controlled knee-loading could be an effective regimen for treating the stated bone-related ailments.

ACS Style

Sai Krishna Prabhala; Stanley Chien; Hiroki Yokota; Sohel Anwar. A Mechatronic Loading Device to Stimulate Bone Growth via a Human Knee. Sensors 2016, 16, 1615 .

AMA Style

Sai Krishna Prabhala, Stanley Chien, Hiroki Yokota, Sohel Anwar. A Mechatronic Loading Device to Stimulate Bone Growth via a Human Knee. Sensors. 2016; 16 (10):1615.

Chicago/Turabian Style

Sai Krishna Prabhala; Stanley Chien; Hiroki Yokota; Sohel Anwar. 2016. "A Mechatronic Loading Device to Stimulate Bone Growth via a Human Knee." Sensors 16, no. 10: 1615.

Journal article
Published: 01 May 2016 in Journal of Power Sources
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In this paper, we propose the design of a novel optimal strategy for charging the lithium-ion battery based on electrochemical battery model that is aimed at improved performance. A performance index that aims at minimizing the charging effort along with a minimum deviation from the rated maximum thresholds for cell temperature and charging current has been defined. The method proposed in this paper aims at achieving a faster charging rate while maintaining safe limits for various battery parameters. Safe operation of the battery is achieved by including the battery bulk temperature as a control component in the performance index which is of critical importance for electric vehicles. Another important aspect of the performance objective proposed here is the efficiency of the algorithm that would allow higher charging rates without compromising the internal electrochemical kinetics of the battery which would prevent abusive conditions, thereby improving the long term durability. A more realistic model, based on battery electro-chemistry has been used for the design of the optimal algorithm as opposed to the conventional equivalent circuit models. To solve the optimization problem, Pontryagins principle has been used which is very effective for constrained optimization problems with both state and input constraints. Simulation results show that the proposed optimal charging algorithm is capable of shortening the charging time of a lithium ion cell while maintaining the temperature constraint when compared with the standard constant current charging. The designed method also maintains the internal states within limits that can avoid abusive operating conditions.

ACS Style

Sourav Pramanik; Sohel Anwar. Electrochemical model based charge optimization for lithium-ion batteries. Journal of Power Sources 2016, 313, 164 -177.

AMA Style

Sourav Pramanik, Sohel Anwar. Electrochemical model based charge optimization for lithium-ion batteries. Journal of Power Sources. 2016; 313 ():164-177.

Chicago/Turabian Style

Sourav Pramanik; Sohel Anwar. 2016. "Electrochemical model based charge optimization for lithium-ion batteries." Journal of Power Sources 313, no. : 164-177.

Journal article
Published: 01 March 2016 in Journal of Power Sources
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In this paper, a gradient-free optimization technique, namely particle swarm optimization (PSO) algorithm, is utilized to identify specific parameters of the electrochemical model of a Lithium-Ion battery with LiCoO2 cathode chemistry. Battery electrochemical model parameters are subject to change under severe or abusive operating conditions resulting in, for example, over-discharged battery, over-charged battery, etc. It is important for a battery management system to have these parameter changes fully captured in a bank of battery models that can be used to monitor battery conditions in real time. Here the PSO methodology has been successfully applied to identify four electrochemical model parameters that exhibit significant variations under severe operating conditions: solid phase diffusion coefficient at the positive electrode (cathode), solid phase diffusion coefficient at the negative electrode (anode), intercalation/de-intercalation reaction rate at the cathode, and intercalation/de-intercalation reaction rate at the anode. The identified model parameters were used to generate the respective battery models for both healthy and degraded batteries. These models were then validated by comparing the model output voltage with the experimental output voltage for the stated operating conditions. The identified Li-Ion battery electrochemical model parameters are within reasonable accuracy as evidenced by the experimental validation results.

ACS Style

Ashiqur Rahman; Sohel Anwar; Afshin Izadian. Electrochemical model parameter identification of a lithium-ion battery using particle swarm optimization method. Journal of Power Sources 2016, 307, 86 -97.

AMA Style

Ashiqur Rahman, Sohel Anwar, Afshin Izadian. Electrochemical model parameter identification of a lithium-ion battery using particle swarm optimization method. Journal of Power Sources. 2016; 307 ():86-97.

Chicago/Turabian Style

Ashiqur Rahman; Sohel Anwar; Afshin Izadian. 2016. "Electrochemical model parameter identification of a lithium-ion battery using particle swarm optimization method." Journal of Power Sources 307, no. : 86-97.

Journal article
Published: 01 December 2014 in Journal of Power Sources
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ACS Style

Amardeep Singh; Afshin Izadian; Sohel Anwar. Model based condition monitoring in lithium-ion batteries. Journal of Power Sources 2014, 268, 459 -468.

AMA Style

Amardeep Singh, Afshin Izadian, Sohel Anwar. Model based condition monitoring in lithium-ion batteries. Journal of Power Sources. 2014; 268 ():459-468.

Chicago/Turabian Style

Amardeep Singh; Afshin Izadian; Sohel Anwar. 2014. "Model based condition monitoring in lithium-ion batteries." Journal of Power Sources 268, no. : 459-468.