Battery Powered Systems

Support: My students and I gratefully acknowledge the following agencies for their generous support:

• National Science Foundation under the NSF/State/Industry/University Center for Low Power Electronics (CLPE), Award EEC-9523338.

Motivation: Cellphone, laptops, tablets, MP3 players, and hundreds upon hundreds of products in daily use by billions of people, operate on batteries.

Most laptops, handheld PCs, and cell phones use rechargeable lithium-ion batteries as their portable energy source. These batteries take anywhere from 1.5 to 4 hours to fully charge, but they can run on this charge for only a few hours or, in the case of some newer tablets and smartphones up to about 10-12 hours. The battery has thus emerged as a key parameter to control in the energy management of portables.

Advances in battery technology have always lagged behind the energy requirements of today’s portable, battery powered digital devices (e.g.. cellphones, PDAs, laptops, etc), which can run many highly computation intesive applications (e.g. video image processing and transmission).

For this reason, there is need to control the load on the battery by proper scheduling of computation threads on the processor and by dynamic voltage and clock frequency scaling. This requires accurate but sufficiently abstract models of the battery discharge characteristics to estimate its lifetime as a function of a time-varying load profile.

Batteries are extremely complex devices, and battery modeling requires a deep understanding of the battery chemistry and the physical and chemical chacteristics of the materials. The standard battery model, known as DualFoil, requires having to specify over a 50 parameters and takes hours to simulate a few seconds of battery operation. For system level analysis and optimization, such a model is not practical.

We developed a analytical model of a battery, that is physically justified (i.e. based on its electro-chemistry) and is parametrically simple so that it can be used to predict the battery lifetime under given discharge conditions, and can also serve as a formal cost function for optimization of the energy usage in battery-powered systems. It involves only two parameters, which are easily estimated for a given battery after conducting several constant-rate discharge tests. The model implicitly accounts for the continuous charge recovery effect that is seen as the load decreases. Extensive experimental evaluation of our model based on numerical simulations of the electrochemical cell, as well as measurements taken on a real battery demonstrated that the model is very accurate and robust.

We next investigated the problem of scheduling of computational threads on a processor (with and without voltage/clock scaling capabilities) so that the resulting discharge profile maximizes the battery lifetime. We derived a scheduling cost function from our analytical battery model and utilized its mathematical properties to develop several novel algorithms for energy-aware task sequencing. These algorithms include, for example, insertion of idle periods to exercise charge recovery, and supply voltage scaling (with the corresponding adjustments of the system clock frequency) to repair battery failures and reduce profile latency.

See UA School of Engineering article: http://uanews.org/story/ua-engineers-are-developing-ways-extend-battery-life-portable-electronics

Publications (Battery Model)

• D. Rakhmatov, S. Vrudhula, and D. Wallach, “A model for battery lifetime analysis for organizing applications on a pocket computer,” IEEE Transactions on VLSI Systems, pp. 1019–1030, December 2003.

• R. Rao, S. Vrudhula, and D. Rakhmatov, “Battery modeling for energy-aware system design,” IEEE Computer, Special issue on Power-Aware & Temperature-Aware Computing, vol. 36, pp. 77–87, December 2003.

• R. Rao, S. Vrudhula, and N. Chang, “Battery optimization vs energy optimization: Which to choose and when?,” in Proceedings of the IEEE International Conference on Computer Aided Design (ICCAD), (San Jose, CA), pp. 439–445, 6-10 November 2005.

• R. Rao, S. Vrudhula, and D. Rakhmatov, “Analysis of discharge techniques for multiple battery systems,” in Proceedings of the IEEE International Symposium on Low Power Electronics and Design (ISLPED), (Seoul, Korea), pp. 44–47, August 2003.

• D. Rakhmatov, S. Vrudhula, and D. Wallach, “Battery lifetime prediction for energy-aware computing,” in Proceedings of the IEEE International Symposium on Low Power Electronics and Design (ISLPED), pp. 154–159, August 2002.

• D. Rakhmatov and S. Vrudhula, “A analytical high-level battery model for use in energy management of portable electronic systems,” in Proceedings of the IEEE International Conference on Computer Aided Design (ICCAD), pp. 488–493, November 2001.

• D. Rakhmatov and S. Vrudhula, “Time-to-failure estimation for bat- teries in portable electronic systems,” in Proceedings of the IEEE International Symposium on Low Power Electronics and Design (ISLPED), (Huntington Beach CA.), pp. 6–7, August 2001.

Publications (Battery Model Based Optimization)

• J. Zhuo, C. Chakrabarti, N. Chang, and S. Vrudhula, “Extending the lifetime of fuel cell based hybrid systems,” in Proceedings of the IEEE Design Automation Conference (DAC), (San Francisco), July 2006.

• S. Dasika, S. Vrudhula, and K. Chopra, “A framework for battery aware sensor management,” in Proceedings of the Design Automation and Test in Europe Conference (DATE), (Paris, France), pp. 962–967, February 2004.

• D. Rakhmatov, S. Vrudhula, and C. Chakrabarti, “Battery-conscious task sequencing for portable devices including voltage/clock scaling,” in Proceedings of the IEEE Design Automation Conference (DAC), June 2002. 189-194.

• J. Zhuo, C. Chakrabarti, K. Lee, N. Chang, and S. Vrudhula, “Maximizing the lifetime of embedded systems powered by fuel cell-battery hybrids,” IEEE Transactions on Very Large Scale Integration (VLSI), vol. 17, no. 1, pp. 22–32, 2009.

• K. Lee, B. Gwon, J. Kim, N. Chang, S. Kadri, S. Vrudhula, J. Zhuo, and C. Chakrabarti, “Design and evaluation of fuel-cell battery hybrid power source for human portbale embedded systems,” ACM Trans- actions on Design Automation of Electronic Systems, vol. 13, no. 1, pp. 19:1–19:34, 2008.

• J. Zhuo, C. Chakrabarti, N. Chang, and S. Vrudhula, “Maximizing the lifetime of embedded systems powered by fuel cell-battery hybrids,” in Proceedings of the IEEE International Symposium on Low Power Electronics and Design (ISLPED), (Tegernsee, Germany), October 2006.

• S. Dasika, S. Vrudhula, and K. Chopra, “Algorithms for optimizing lifetime of battery powered wireless sensor networks,” Sensor Network Operations, IEEE Press, May 2006.

• D. Rakhmatov and S. Vrudhula, “Energy management for battery- powered embedded systems,” ACM Transactions on Embedded Com- puting Systems, vol. 2, pp. 277–324, August 2003.