Active Cell Balancing based on DC-DC converter has become prominent due to its improved balancing accuracy, high energy conversion efficiency, and modularized approaches to balancing the charge in cells. It is widely employed in emerging applications such as electric vehicles, UAVs, and renewable grid storage to enhance battery life. The state-of-the-art active cell balancing techniques employ separate cell balancing for series and parallel connected cells. However, during separate balancing of cells in series and parallel configuration, the cell balancing system suffers from high system latency, low balancing speed, and higher power consumption due to a large number of balancing components. This article introduces a simultaneous series-parallel Flyback converter-based PWM duty cycle controlled Active Cell Balancing methodology with Constant Current-Constant Voltage (CC-CV) charging/discharging. The battery pack model was simulated with capacity and State-of-Charge (SOC) imbalances. The results show an improvement of 32.5% in balancing time and 81.47% reduction in power consumption without compromising the balancing accuracy when compared to balancing series and parallel cells separately in state-of-the-art active cell balancing methodologies. This helps to prolong battery lifetime and improve safety.

Real-time and accurate estimation of battery internal states is immensely critical for emerging applications such as Electric Vehicles (EV), smart grids, and space applications. Model-based state estimation methodology provides highly robust and accurate battery state estimation. However, separate estimation of states, such as State-of-Charge (SOC), State-of-Health (SOH), and State-of-Power (SOP), leads to erroneous estimation since the states are highly interdependent. A co-estimation methodology for SOC, SOH, and SOP using a highly accurate and stable formulation of the Kalman filter, i.e., the Dual Square Root Unscented Kalman filter (D-SRUKF) is proposed in this paper. The proposed battery states co-estimation methodology has been validated using experimental battery test data. The results show that SOC estimation error is 0.404 %, with an improvement of 77.60% compared to separate state estimation using the D-SRUKF estimator and 58.02% compared to state-of-the-art EKF-RLS co-estimation methodology. SOH and SOP are also co-estimated within the same filter, leading to accurate estimation without adding to the computational complexity of the system. The accuracy of SOH estimation is improved by 16.98% compared to the EKF-RLS co-estimation.

Single channel Blind Source Separation (SCBSS) is a challenging problem for several real-world practical applications. The existing SCBSS methodologies depend upon the properties of the sources present in the mixture and hence do not remain truly blind. Also, the solutions are found to be suboptimal and limited in application. In this paper, we present an SCBSS methodology using a state-parameter estimation approach to eliminate the constraints on the source signals such as statistical independence and frequency disjoint spectra. A Dual Square Root Unscented Kalman Filter (D-SRUKF) estimator has been proposed, which demonstrates higher numerical accuracy and improved stability compared to the widely used Dual Extended Kalman Filter (D-EKF). Simulations have been performed for separating mixed signals with overlapping spectra such as speech and biomedical signals. The proposed methodology demonstrates higher Signal-to-Interference Ratio (SIR) and Signal-to-Distortion Ratio (SDR) when the current methodologies even fail to separate the sources. The results also show that the proposed DSRUKF SCBSS is 15% more accurate than the state-of-the-art D-EKF SCBSS and has higher stability owing to the square root formulation of D-SRUKF source estimator.

Single channel Blind Source Separation (SCBSS) is an important source separation technique gaining prominence in many emerging applications. It is a special case of the well-defined Blind Source Separation (BSS) where only a single mixed signal is recorded to estimate the unknown sources. In this paper, we propose a simultaneous state-parameter estimation methodology for SCBSS using Dual Extended Kalman Filter (D-EKF). The proposed methodology eliminates the inherent frequency disjoint and statistical independence limitations of the state-of-the-art SCBSS approaches such as single channel Independent Component Analysis (SCICA). A frame- based Kalman processing technique has been proposed to ensure faster convergence of the proposed methodology. Simulation results have been presented for mixed sources with overlapping spectra and compared with SCICA and other BSS algorithms. The results demonstrate the superior performance of the proposed methodology with improved Signal-to-Interference Ratio (SIR) and Signal-to-Distortion Ratio (SDR) for real-world practical applications.

Recycled ICs have become a major threat to the ICs used in safety critical systems. In the current state-of-the-art techniques, recycled ICs are detected by measuring the frequency, current, path delay or power-up values to estimate the HCI, BTI and EM effects on the transistors with age. Some of the state- of-the-art techniques require additional on-chip sensors to detect and estimate the age of an IC while others use existing logic like SRAM and Flip-flops to detect the recycled ICs. In this paper, we provide a methodology to detect a recycled IC and also to estimate its age by using the existing IO pad structures. For the first time, age is estimated by measuring voltage drop across the protection diodes present in IO pad structure. With this methodology, no additional sensors have to be added and hence there is no area overhead. With this proposed methodology, ICs that are used for a minimum period of a day can be effectively detected by using the concept of extended Kalman filtering technique for the first time in this domain. By stressing the part for five days, our proposed methodology can estimate the age of the IC aged between 1 month to 5 years with 95% percent of accuracy.

With an increased focus on green transportation, hybrid energy storage systems (HESS) for Electric Vehicles (EV) are gaining importance in recent years. In this paper, we propose a control strategy for optimal distribution of power demand between battery and supercapacitor (SC) in a HESS, as well as efficient utilization of the regenerative braking power. A DC/DC boost converter based power splitting strategy has been proposed. Two control blocks are proposed for charging and discharging of energy storage, which reduces the rate of discharge current, thereby improving the health and life of the battery. Simulation results are presented for the proposed circuit and compared with the state-of-the-art topology. The results show that the proposed control strategy improves State-of-Charge (SOC) of the battery from 84% to 94.4% in comparison with only battery held systems. The depth of discharge (DOD) also improved by 10.4%, which helps to enhance the range of the HESS based EV from a single charge.

Real-time and accurate estimation of battery states has gained immense importance in recent years due to emerging applications of Battery Energy Storage Systems (BESS) in smart grid and Electric Vehicles. The behaviour of BESS modelled as a 2-RC Circuit and State-of-Charge (SOC) and RC parameter estimation using Unscented Kalman Filter (UKF) has emerged as an optimal model for online Battery Management Systems (BMS). However, the stability of UKF degrades due to error covariance matrix becoming ill-conditioned. This paper presents a dual Square Root Unscented Kalman Filter (SRUKF) based SOC and parameter estimation algorithms for BMS. The proposed SRUKF methodology improves the stability of the system as the square root form of the error covariance matrix always remains positive semi-definite. The methodology has been designed and implemented in MATLAB/Simulink and compared with dual EKF and state-of-the-art dual UKF algorithms. The results show that dual SRUKF is 74% more accurate than the state-of-the-art and remains stable once it converges to true SOC value.

The recent progressions in semiconductor and computing technology have empowered the PPG utilization in medical diagnosis. This paper presents a reconfigurable deep learning framework `CardioNet' for early diagnosis of cardiovascular risk factors or most common diseases (such as diabetes, hypertension, cerebrovascular, cerebra-infraction) using the PPG data. The proposed model has a light-weight architecture, designed by exploiting the deep learning framework of convolutional neural network, exhibiting inherent capability of feature extraction, thereby, eliminating the cost effective steps of feature selection and extraction. The performance demonstration of the proposed model is done on a healthy dataset comprising 657 data segments of 219 subjects holding records of common CVD risk factors (diabetes, hypertension, cerebrovascular, cerebra-infraction). The obtained results of an overall accuracy of 97% for diagnosis of CVD risk factors, show the efficiency of the proposed model for real-time usability. The clinical significance of this work to provide an accurate and non-invasive method for early diagnosis and monitoring of cardio-risk factors.