Simulation and performance analysis of self-powered piezoelectric energy harvesting system for low power applications

Energy harvesting is a process of extracting energy from surrounding environments. The extracted energy is stored in the supply power for various applications like wearable, wireless sensor, and internet of thing (IoT) applications. The electricity generation using conventional approaches is very costly and causes more pollution in the environmental surroundings. In this manuscript, an energy-efficient, self-powered battery-less piezoelectric-based energy harvester (PE-EH) system is modeled using maximum power point tracking (MPPT) module. The MPPT is used to track the optimal voltage generated by the piezoelectric (PE) sensor and stored across the capacitor. The proposed PE system is self-operated without additional microarchitecture to harvest the Power. The experimental simulation results for the overall PE-EH systems are analyzed for different frequency ranges with variable input source vibrations. The optimal voltage storage across the storing capacitor varies from 1.12 to 1.6 V. The PE-EH system can harvest power up to 86 µW without using any voltage source and is suitable for low-power applications. The proposed PE-EH module is compared with the existing similar EH system with better improvement in harvested power

An efficient hybrid biomechanical energy harvesting system using human motions for low-power applications

The biomechanical energy harvesting system (BM-EHS) uses human daily activities to create electricity. The BM-EHS is one of the potential alternative technologies for powering wearable and implantable electronic gadgets without batteries. The hybrid BH-EHS is modeled using two different vibration source-based human activities in this manuscript. The piezoelectric (PE) and electromagnetic (EM) based EHS are combined in the hybrid BM-EHS. The PE- EHS is based on human walking and jogging motions and is represented using a mass-spring-damper system and PE stack. The EM- EHS is based on the human knee and hip motions, with shaft conversion and a DC motor. The PE, EM, and hybrid BM-based EHS are modeled using MATLAB/Simulink, and performance results are realized individually. The PE-EHS obtains the average output voltage of 0.5 V and harvests 53.18 mW of power. Similarly, the EM-EHS achieves the average load voltage of 0.567 V and 30.6 mW harvested power. The hybrid BMEHS obtains the average load voltage of 0.79 V and harvests 86 mW of power. The proposed BM-EHS is compared with the existing EHS with better-harvested power and energy improvement for the given load conditions. Overall, the harvested power can power up the low-power applications