Continued emission of green house gases has energized research activity worldwide to develop efficient ways to harness renewal energy. The availability of large scale energy storage technologies is essential to make renewal energy a reliable source of energy. Redox flow batteries show potential in this direction. These batteries typically need expensive membranes which need replacement be-cause of fouling. The recently proposed soluble lead redox flow battery (SLRFB), in which lead ions deposit on electrodes in charge cycle and dissolve back in discharge cycle, can potentially cut down the cost of energy storage by eliminating membrane. A number of challenges need to be overcome though. Low cycleability, residue formation, and low efficiencies are foremost among these, all of which require an understanding of the underlying mechanisms. A model of laminar flow-through SLRFB is first developed to understand buildup of residue on electrodes with continued cycling. The model accounts for spatially and temporally growing concentration boundary layers on electrodes in a self consistent manner by permitting local deposition/dissolution rates to be controlled by local ion transport and reaction conditions. The model suggests controlling role for charge transfer reaction on electrodes (anode in particular) and movement of ions in the bulk and concentration boundary layers. The non-uniform current density on electrodes emerges as key to formation of bare patches, steep decrease in voltage marking the end of discharge cycle, and residue buildup with continuing cycles. The model captures the experimental observations very well, and points to improved operational efficiency and decreased residue build up with cylindrical electrodes and alternating flow direction of recirculation. The underlying mechanism for more than an order of magnitude increase in cycle life of a beaker cell battery with increase in stirrer speed is unraveled next. Our experiments show that charging with and without stirring occurs identically, which brings up the hitherto unknown but quite strong role of natural convection in SLRFB. The role of stirring is determined to be dislodgement/disintegration of residue building up on electrodes. The depletion of active material from electrolyte due to residue formation is offset by “internal regeneration mechanism”, unraveled in the present work. When the rate of residue formation, rate of dislodging/disintegration from electrode, and rate of regeneration of active material in bulk of the electrolyte becomes equal, perpetual operation of SLRFB is expected. The identification of strong role of free convection in battery is put to use to demonstrate a battery that requires stirring/mixing only intermittently, during open circuit stages between charge and discharge cycles when no current is drawn. Inspired by our experimental finding that the measured currents for apparently diffusion limited situations (no external flow) are far larger than the maxi-mum possible theoretical value, the earlier model is modified to account for natural convection driven by concentration gradient of lead ions in electrolyte. The model reveals the presence of strong natural convection in battery. The induced flow in the vicinity of the electrodes enhances mass transport rates substantially, to the extent that even in the absence of external flow, normal charge/discharge of battery is predicted. The model predicted electrochemical characteristics are verified quantitatively through voltage-time measurements. The formation of flow circulation loops driven by electrode processes is validated qualitatively through PIV measurements. Natural convection is predicted to play a significant role in the presence of external flow as well. The hitherto unexplained finding in the literature on insensitivity of charge-discharge characteristics to electrolyte flow rate is captured by the model when mixed mode of convection is invoked. Flow reversal and wavy flow are predicted when natural convection and forced convection act in opposite directions in the battery. The effect of the presence of non-conducting material (PbO on anode) on the performance of SLRFB is studied using a simplified approach in the model. The study reveals the presence of charge coup de fouet phenomenon in charge cycle. The phenomenon as well as the predicted effect of depth of discharge on the magnitude of charge coup de fouet are confirmed experimentally.

Continued emission of green house gases has energized research activity worldwide to develop efficient ways to harness renewal energy. The availability of large scale energy storage technologies is essential to make renewal energy a reliable source of energy. Redox flow batteries show potential in this direction. These batteries typically need expensive membranes which need replacement be-cause of fouling. The recently proposed soluble lead redox flow battery (SLRFB), in which lead ions deposit on electrodes in charge cycle and dissolve back in discharge cycle, can potentially cut down the cost of energy storage by eliminating membrane. A number of challenges need to be overcome though. Low cycleability, residue formation, and low efficiencies are foremost among these, all of which require an understanding of the underlying mechanisms. A model of laminar flow-through SLRFB is first developed to understand buildup of residue on electrodes with continued cycling. The model accounts for spatially and temporally growing concentration boundary layers on electrodes in a self consistent manner by permitting local deposition/dissolution rates to be controlled by local ion transport and reaction conditions. The model suggests controlling role for charge transfer reaction on electrodes (anode in particular) and movement of ions in the bulk and concentration boundary layers. The non-uniform current density on electrodes emerges as key to formation of bare patches, steep decrease in voltage marking the end of discharge cycle, and residue buildup with continuing cycles. The model captures the experimental observations very well, and points to improved operational efficiency and decreased residue build up with cylindrical electrodes and alternating flow direction of recirculation. The underlying mechanism for more than an order of magnitude increase in cycle life of a beaker cell battery with increase in stirrer speed is unraveled next. Our experiments show that charging with and without stirring occurs identically, which brings up the hitherto unknown but quite strong role of natural convection in SLRFB. The role of stirring is determined to be dislodgement/disintegration of residue building up on electrodes. The depletion of active material from electrolyte due to residue formation is offset by “internal regeneration mechanism”, unraveled in the present work. When the rate of residue formation, rate of dislodging/disintegration from electrode, and rate of regeneration of active material in bulk of the electrolyte becomes equal, perpetual operation of SLRFB is expected. The identification of strong role of free convection in battery is put to use to demonstrate a battery that requires stirring/mixing only intermittently, during open circuit stages between charge and discharge cycles when no current is drawn. Inspired by our experimental finding that the measured currents for apparently diffusion limited situations (no external flow) are far larger than the maxi-mum possible theoretical value, the earlier model is modified to account for natural convection driven by concentration gradient of lead ions in electrolyte. The model reveals the presence of strong natural convection in battery. The induced flow in the vicinity of the electrodes enhances mass transport rates substantially, to the extent that even in the absence of external flow, normal charge/discharge of battery is predicted. The model predicted electrochemical characteristics are verified quantitatively through voltage-time measurements. The formation of flow circulation loops driven by electrode processes is validated qualitatively through PIV measurements. Natural convection is predicted to play a significant role in the presence of external flow as well. The hitherto unexplained finding in the literature on insensitivity of charge-discharge characteristics to electrolyte flow rate is captured by the model when mixed mode of convection is invoked. Flow reversal and wavy flow are predicted when natural convection and forced convection act in opposite directions in the battery. The effect of the presence of non-conducting material (PbO on anode) on the performance of SLRFB is studied using a simplified approach in the model. The study reveals the presence of charge coup de fouet phenomenon in charge cycle. The phenomenon as well as the predicted effect of depth of discharge on the magnitude of charge coup de fouet are confirmed experimentally.

The growing urgency of climate change has led to growth in the electrification technology field, where batteries have emerged as an essential role in the renewable energy transition, supporting the implementation of environmentally friendly technologies such as smart grids, energy storage systems, and electric vehicles. Battery cell degradation is a common occurrence indicating battery usage. Optimizing lithium-ion battery degradation during operation benefits the prediction of future degradation, minimizing the degradation mechanisms that result in power fade and capacity fade. This degree project aims to investigate battery degradation prediction based on capacity using deep learning methods. Through analysis of battery degradation and health prediction for lithium-ion cells using non-destructive techniques. Such as electrochemical impedance spectroscopy obtaining ECM and three different deep learning models using multi-channel data. Additionally, the AI models were designed and developed using multi-channel data and evaluated performance within MATLAB. The results reveal an increased resistance from EIS measurements as an indicator of ongoing battery aging processes such as loss o active materials, solid-electrolyte interphase thickening, and lithium plating. The AI models demonstrate accurate capacity estimation, with the LSTM model revealing exceptional performance based on the model evaluation with RMSE. These findings highlight the importance of carefully managing battery charging processes and considering factors contributing to degradation. Understanding degradation mechanisms enables the development of strategies to mitigate aging processes and extend battery lifespan, ultimately leading to improved performance.