Research On Solar Micro-Energy Harvesting And Management Of Wearable Devices

As the functions of wearable devices become more and more diversified,the power consumption of the wearable devices to complete various sensing and calculations has also increased proportionally.However,the small size and limited space to meet ergonomic design requirements make the battery of the wearable devices unable to expand proportionally.Before the development of battery technology with greater power density,the limited battery life is still the "Achilles' heel" that limits the development of wearable technology.Micro-energy harvesting technology is a technology that uses a suitable energy harvesting device to collect green energy in the environment and convert it into electrical energy for powering wearable devices,which can greatly improve the endurance of wearable devices.This dissertation uses wearable devices as the application scenario,focusing on the goal of designing high-performance solar micro-energy harvesting and management systems.Firstly,in this dissertation,the performance optimization ideas and specific design methods of high-performance micro-energy harvesting and management systems were introduced;secondly,a high-precision explicit model of solar cells and a power coupling model in the solar micro-energy harvesting and management systems were developed;then,the design and verification of two high-performance solar microenergy harvesting and management chips were completed based on the optimization ideas of broaden sources and reduce consumption,the assistance of artificial neural networks and the guidance of the power coupling model.The main innovations include:(1)A new and high-precision explicit model of solar cells was proposed.This explicit model used exponential expression to approximate the original nonlinear equation.After four unknown parameters related to light intensity and temperature were determined,a high-precision explicit model with root mean square error of 6.02e-5 was established.It had guiding significance for circuit design and performance optimization;(2)The idea of combining the solar cell model and the power converter input model into one was proposed,and the accurate single-gain and multi-gain power coupling model was established.The power coupling model linked the electrical characteristic parameters of the solar cell with the circuit design parameters in one model.The average error of the single-gain power coupling model was 0.31%,and the average errors of the multi-gain power coupling model were 0.275%,0.195%,0.4% and 0.08%,respectively.The results showed that the model had a fairly high simulation accuracy and can provide a theoretical basis for the design and optimization of micro-energy harvesting and management circuits;(3)An artificial neural network assisted adaptive maximum power point tracking strategy based on a reconfigurable voltage-controlled oscillator was proposed.This strategy combined the single-gain power coupling model and the hybrid simulation method based on artificial neural network.And the negative feedback control loop was constructed through a reconfigurable voltage-controlled oscillator to achieve low-cost,high-precision tracking adaptly to the maximum power point of solar cells.The design and implementation of a low-cost,reconfigurable,high-precision adaptive maximum power point chip were completed based on the strategy.The average tracking errors of two different monocrystalline silicon solar cells were only 0.24% and 0.29%,and the peak conversion efficiencies were 89.39% and 83.03% respectively.The results showed that this chip had the advantages of low cost,high versatility and high precision;(4)A time-sharing control micro-energy harvesting and management strategy assisted by energy storage elements and efficiency optimization method for multi-gain DC-DC converter were proposed.This strategy used energy storage components as an auxiliary,adaptive maximum power point tracking method and efficiency optimization method based on multi-gain power coupling model and time-sharing control energy management strategy were adopted,the design and implementation of micro-energy harvesting and management chip with high precision,high efficiency was completed.Under the conditions of 3.2 V and 3.6 V output voltage for monocrystalline silicon and amorphous-silicon flexible solar cells,the test errors of adaptive maximum power point tracking were 0.36%,0.32%,0.44% and 0.31%,and the average conversion efficiency were 79.66%,74.7%,74.44% and 70.32%.The test results of the chip and self-powered system showed that the chip had excellent performance of tracking accuracy and conversion efficiency,and can greatly improve the battery life of wireless sensors for wearable devices.