Muhammad Rizwan Azam

Cell balancing has a paramount significance in a battery management system (BMS) for improving battery performance and safety. Performance characterization of different active cell balancing networks (ACBNs) is important to understand their utilization. In this work, switching capacitor (SC), buck-boost (BB), and flyback (FB) ACBNs with static and dynamic parameters are quantitatively analyzed during charging/discharging and static conditions. For this purpose, a high-fidelity mean current modeling approach is employed, and an urban dynamometer driving schedule (UDDS) input current profile is used. The analysis of each ACBN is verified by incorporating a practical scenario in Simscape™toolbox of SIMULINK. A state dependent Kalman Filter (SDKF) is also designed to estimate the state of charge (SoC), as precise estimation of the SoC is critical for determining the performance of any balancing architecture. The sensitivity analysis manifests that consideration of static and dynamic parameters results in a percentage change of 26.75%, 80%, and 108% in the cell balancing speed of SC, BB, and FB, respectively. Furthermore, it is observed that the trade-off exhibited in the efficiency and cell balancing speed of balancing architectures. It is worth observing that the SC network is efficient but slower than BB and FB cell balancing techniques. While the BB is slower than FB but almost similar in terms of efficiency.

Due to its inherent robustness and finite time convergence, sliding mode control (SMC) is extensively used for the control of nonlinear uncertain systems

The p53 pathway has been the focus of many researchers in the last few decades owing to its pivotal role as a frontline cancer suppressant protein. It plays a vital role in maintaining cell cycle checkpoints and cell apoptosis in response to a broken DNA strand. This is why it is found in the mutated form in more than 50% of malignant tumors. To overcome this, various drugs have been proposed to revive the p53 pathway in cancer patients. Small-molecule-based drugs, such as Nutlin 3a, which are capable of performing this stimulation, are at the fore of advanced clinical trials. However, the calculation of their dosage is a challenge. In this work, a method to determine the dosage of Nutlin 3a is investigated. A control-systems-based model is developed to study the response of the wild-type p53 protein to this drug. The proposed strategy regulates the p53 protein along with negative and positive feedback loops mediated by the MDM2 and MDM2 mRNA, respectively, along with the reversible repression of MDM2 caused by Nutlin 3a. For a broader perspective, the reported PBK dynamics of Nutlin 3a are also incorporated. It has been reported that p53 responds to stresses in two ways in terms of concentration to this drug: either it is a sustained (constant) or an oscillatory response. The claimed dosage strategy turned out to be appropriate for sustained p53 response. However, for the induction of oscillations, inhibition of MDM2 is not enough; rather, anti-repression of the p53–MDM2 complex is also needed, which opens new horizons for a new drug design paradigm.