Safety of Lithium Nickel Cobalt Aluminum Oxide Battery Packs in Transit Bus Applications

The future of mass transportation is clearly moving toward the increased efficiency and greenhouse gas reduction of hybrid and electric vehicles. With the introduction of high-power/high-energy storage devices such as lithium ion battery systems serving as a key element in the system, valid safety and security concerns emerge. This is especially true when the attractive high-specific-energy and power-chemistry lithium nickel cobalt aluminum oxide (NCA) is used. This chemistry provides great performance but presents a safety and security risk when used in large quantities, such as for a large passenger bus. If triggered, the cell can completely fuel its own fire, and this triggering event occurs more easily than one may think. To assist engineers and technicians in this transfer from the use of primarily fossil fuels to battery energy storage on passenger buses, the Battery Application Technology Testing and Energy Research Laboratory (BATTERY) of the Thomas D. Larson Pennsylvania Transportation Institute (LTI) in the College of Engineering at The Pennsylvania State University partnered with advanced chemistry battery and material manufacturers to study the safety concerns of an NCA battery chemistry for use in transit buses. The research team ran various experiments on cells and modules, studying rarely considered thermal events or venting events. Special considerations were made to gather supporting information to help better understand what happens, and most importantly how to best mitigate these events and/or manage them when they occur on a passenger bus. The research team found that the greatest safety concern when using such a high-energy chemistry is ensuring passenger safety when a cell’s electrolyte boils and causes the ventilation of high-temperature toxic material. A cell-venting event can be triggered by a variety of scenarios with differing levels of likelihood. Also, though the duration of a venting event is relatively short, on the order of just a few seconds, the temperature of the venting material and cell is extremely high. During a venting event, the high-pressure, burning gases tend to burn holes in nearby packaging materials. Most interestingly, the team discovered that following a venting event the large-format cells tested immediately reached and remained at extremely high external skin temperatures for very long periods, on the order of hours. The majority of this report covers the testing designed to better understand how high-energy cells of this chemistry fail and what materials can be used to manage these failures in a way that increases passenger survivability

Penn State DOE GATE Program

The Graduate Automotive Technology Education (GATE) Program at The Pennsylvania State University (Penn State) was established in October 1998 pursuant to an award from the U.S. Department of Energy (U.S. DOE). The focus area of the Penn State GATE Program is advanced energy storage systems for electric and hybrid vehicles

Penn State DOE GATE Program

The Graduate Automotive Technology Education (GATE) Program at The Pennsylvania State University (Penn State) was established in October 1998 pursuant to an award from the U.S. Department of Energy (U.S. DOE). The focus area of the Penn State GATE Program is advanced energy storage systems for electric and hybrid vehicles

Exploiting Differential Flatness and Pseudospectral Optimization to Improve the Computational Efficiency of Health-Conscious Lithium-Ion Battery Control

This paper examines the health-conscious optimal control of lithium-ion batteries. The paper focuses specifically on the optimizing battery control policies to minimize solid electrolyte interphase (SEI) layer growth. However, its approach is applicable to other degradation mechanisms. The paper exploits the fact, established by the mathematical control literature1–4, that Fick’s second law of diffusion is differential flat for both linear and nonlinear diffusion problems. Differential flatness only applies to each diffusion medium in the battery separately. In the single particle model (SPM), for instance, each electrode’s diffusion sub-model alone is differential flat. Ensuring differential flatness for the entire battery is a two-step process. First, one must truncate redundant integrators from the two electrodes’ diffusion sub-models. Second, one must then relate the truncated integral state variable to the remaining state variables through an affine transformation. The differential flatness of the resulting battery model makes it possible to solve the health-conscious battery management problem efficiently using pseudospectral methods. This exploitation of the structure (i.e., differential flatness) of lithium-ion battery dynamics for more computationally efficient trajectory optimization is novel and significant. The existing literature already examines the problem of health-conscious optimal battery charging and discharging5,6. However, the optimal control tools used in this literature are not tailored to exploit the structure (e.g., differential flatness) of lithium-ion battery models. This can lead to higher computational burdens compared to the novel and more efficient approach we present here. The approach has the added advantage that it can be used for both offline trajectory optimization and online model-predictive control. The paper demonstrates the above ideas using the SPM. The optimization problem is formulated by equation [1-6] and the SPM with film growth model. The cost function in Equation [1] aims to track the reference state of charge (SOC) and at the same time minimize the film growth rate, which is defined by equation [7]-[14] in Ramadass’s paper7. The weight β represents a tradeoff/balance between aggressive charging and battery degradation. Constraints [2-6] place limits on battery charge/discharge current, reflecting battery management hardware capabilities. Furthermore, these constraints limit battery SOC to prevent over-charging and over-discharging. Finally, these constraints also place bounds on battery state variables contributing to damage phenomena such as lithium plating and mechanical degradation. For instance, we bound the concentration gradients in the battery cell and the overpotentials driving the lithium plating side reaction8–10. The optimization approach presented in this paper transforms these equations and constraints from a dynamic programming problem to a nonlinear programming (NLP) problem. Solving this NLP problem using traditional optimization methods leads to a health-conscious battery management policy. The value of this paper lies not in this policy, but rather in the computational efficiency with which it is obtained. Specifically, to the best of the authors’ knowledge, this paper represents the first attempt to exploit lithium-ion battery model structure for more efficient solution of the health-conscious optimal management problem. ACKNOWLEDGMENTS The research was funded by ARPA-E AMPED program grant # 0675-1565. The authors gratefully acknowledge this support. REFERENCES 1. M. FLIESS, J. LÉVINE, P. MARTIN, and P. ROUCHON, Int. J. Control, 61, 1327–1361 (1995). 2. B. Laroche, P. Martin, and P. Rouchon, Int. J. Robust Nonlinear Control, 10, 629–643 (2000). 3. M. Fliess and R. Marquez, Int. J. Control, 73, 606–623 (2000). 4. T. Meurer, Automatica, 47, 935–949 (2011). 5. R. Klein and N. Chaturvedi, Am. Control … (2011). 6. V. Boovaragavan and V. R. Subramanian, J. Power Sources, 173, 1006–1011 (2007). 7. P. Ramadass, B. Haran, P. M. Gomadam, R. White, and B. N. Popov, J. Electrochem. Soc., 151, A196 (2004). 8. J. Christensen and J. Newman, J. Solid State Electrochem., 10, 293–319 (2006). 9. X. Zhang, A. M. Sastry, and W. Shyy, J. Electrochem. Soc., 155, A542 (2008). 10. N. Chaturvedi, R. Klein, J. Christensen, J. Ahmed, and A. Kojic, IEEE Control Syst. Mag., 30, 49–68 (2010). </jats:p

HIL Development of Control Algorithm for Vehicle Demand-Driven Production of H2 From an Al/Mg and H2O Reaction

This technical paper provides instruction by example on how to apply hardware-in-the-loop (HIL) simulation for accelerated development of a complex control algorithm. The instruction provided in this technical paper is directed to HIL test bench setup, software, simulated and real hardware, and test methods. As an example, the authors reference their collaborative development project of the last couple of years, now completed. The objective of that project was to develop a demand-driven hydrogen production system and integrate it with a hydrogen-fueled internal combustion engine-powered vehicle test platform. The instruction provided in this technical paper is supported by data from the referenced project example.</jats:p