Modeling, real-time degradation identification, and remediation of pb-acid batteries

Valve Regulated Lead-Acid (VRLA) batteries are cheap and mature technology, favorable candidates for micro-hybrid vehicles and stationary applications. Large-scale battery packs, instead of individual cells, are implemented in those applications; therefore sophisticated battery management system (BMS) becomes necessary and crucial to ensure the longevity and efficient utilization of battery packs.;This research first reviews six modeling techniques that are suitable for developing electrochemistry-based system models of batteries. Fundamental battery models, consisting of nonlinear coupled partial differential equations, are often difficult to discretize and reduce in order so that they can be used by systems engineers for design, estimation, prediction, and management. I.;Secondly, this research presents a nondestructive experiment method to perform real-time aging diagnosis of lead-acid batteries. VTLA batteries can degrade due to a variety of mechanisms, including corrosion, hard sulfation, water loss, shedding, and active mass degradation. VRLA batteries are designed to minimize these effects as much as possible but the operating environment, cell-to-cell and battery-to-battery manufacturing variations, and use can cause different degradation mechanisms to dominate capacity loss and/or impedance rise. With accurate State of Health monitoring, cell usage can be adjusted by the battery management system (BMS) to optimize the performance and life of the energy storage system. The BMS must be able to determine in real time the predominant degradation mechanism for each cell and adjust use accordingly. In this work, new and dead VRLA batteries are tested with constant, sinusoidal, and pulse charge/discharge current inputs while measuring the cell voltage and pressure to determine the cause of death of the cells. As expected, the new cells have fairly uniform performance with limited signs of degradation. The cells in the dead battery, however, have widely ranging performance, especially at the end of discharge and charge. Analysis of the charge/discharge data indicate that three cells died of water loss and a fourth cell died of sulfation. The remaining two cells were fairly healthy but will accompany their dead companions to the recycling center nonetheless. While the full charge/discharge data provided useful forensic pathology data, EIS and pulse charge/discharge data varied with aging mechanisms and only provided supplementary pathology information.;Following the real-time diagnosis work, a charging control scheme is proposed that removes hard sulfation in lead-acid cells without introducing excessive gassing. In a battery string, the cell with the lowest capacity dominants that of the entire string. If that cell's capacity can be recovered, the capacity of the whole string will increase. However, not all aging mechanisms in lead-acid batteries are reversible but hard sulfation is. Often, removal of one degradation mechanism might worsen another. In this study, it appears that one cell of a 6-cell string died from sulfation and another three from dehydration. The battery capacity is mainly dictated by the sulfated cell. A desulfation charging control scheme with pressure feedback is designed to break up hard sulfate and recover capacity while minimizing water loss by using low current charging. The capacity of the cell is recovered by 41% with minimal water loss, demonstrating the effectiveness of the desulfation charge controller.;The experiments reveal the great potential of charge strategies with pressure control. To make this health-cautious charge more cost-effective and easy to implement, a nonlinear system model is developed, aiming to eliminate the pressure transducers by covering gassing side reactions in the model. The system model is fifth-order with parameters and states that are based on the electrochemical processes and battery properties. It preserves the majority of the underlying complex mathematical model but enjoys the beauty of state space form, which eases future controllers and estimators design. The model is validated with testing data and shows good match in battery voltage and pressure responses. It also returns the internal states such as acid concentration, solidphase potentials, and transfer current densities. Those states can be used for control in the future. (Abstract shortened by UMI.).