ABSTRACT
Different battery chemistries perform better on different axes, such as energy density, cost, peak power, recharge time, longevity, and efficiency. Mobile system designers are constrained by existing technology, and are forced to select a single chemistry that best meets their diverse needs, thereby compromising other desirable features. In this paper, we present a new hardware-software system, called Software Defined Battery (SDB), which allows system designers to integrate batteries of different chemistries. SDB exposes APIs to the operating system which control the amount of charge flowing in and out of each battery, enabling it to dynamically trade one battery property for another depending on Application And/Or User Needs. Using microbenchmarks from our prototype SDB implementation, and through detailed simulations, we demonstrate that it is possible to combine batteries which individually excel along different axes to deliver an enhanced collective performance when compared to traditional battery packs.
Supplemental Material
- Altium Designer. http://www.altium.com/altium-designer/overview.Google Scholar
- Apple MacBook (2015) Battery Design. http://www.apple.com/macbook/design/.Google Scholar
- Arbin BT-2000 Battery Testing Equipment. http://www.arbin.com/products/battery.Google Scholar
- N. Balasubramanian, A. Balasubramanian, and A. Venkataramani. Energy consumption in mobile phones: A measurement study and implications for network applications. In Proc. IMC'09, Chicago, Illinois, Nov. 2009. Google ScholarDigital Library
- Battery Univeristy: The high-power Lithium ion. http://batteryuniversity.com/index.php/learn/article/the_high_power_lithium_ion.Google Scholar
- J. Bickford, H. A. Lagar-Cavilla, A. Varshavsky, V. Ganapathy, and L. Iftode. Security versus Energy Tradeoffs in Host-Based Mobile Malware Detection. In Proc. 9th ACM MobiSys, Washington, DC, June 2011. Google ScholarDigital Library
- A. Carroll and G. Heiser. An Analysis of Power Consumption in a Smartphone. In Proc. USENIX ATC, Boston, MA, June 2010. Google ScholarDigital Library
- M. Chen and G. A. Rincon-Mora. Accurate Electrical Battery Model Capable of Predicting Runtim and IV Performance. IEEE Trans. Energy Conversion, 21(2):504--511, 2006.Google ScholarCross Ref
- C.-F. Chiasserini and R. R. Rao. Energy efficient battery management. IEEE Journal. Selected Areas in Communications, 19(7):1235--1245, 2001. Google ScholarDigital Library
- Cortana Personal Assistant. https://www.windowsphone.com/en-us/how-to/wp8/cortana/meet-cortana?signin=true.Google Scholar
- M. Dong and L. Zhong. Self-Constructive High-Rate System Energy Modeling for Battery-Powered Mobile Systems. In Proc. 9th ACM MobiSys, Washington, DC, June 2011. Google ScholarDigital Library
- O. Erdinc, B. Vural, and M. Uzunoglu. A dynamic lithium-ion battery model considering the effects of temperature and capacity fading. In Proc. IEEE International Conference on Clean Electrical Power, Capri, Italy, June 2009.Google ScholarCross Ref
- External Battery Packs. http://www.ianker.com/External%20Batteries/category-c1-s1.Google Scholar
- J. Flinn and M. Satyanarayanan. Energy-Aware Adaptation of Mobile Applications. In Proc. 17th ACM SOSP, Charleston, SC, Dec. 1999. Google ScholarDigital Library
- R. Fonseca, P. Dutta, P. Levis, and I. Stoica. Quanto: Tracking Energy in Networked Embedded Systems. In Proc. 8th USENIX OSDI, San Diego, CA, Dec. 2008. Google ScholarDigital Library
- L. Gao, S. Liu, and R. A. Dougal. Dynamic lithium-ion battery model for system simulation. IEEE Trans. Components and Packaging Technologies, 25(3):495--505, 2002.Google ScholarCross Ref
- Google Now. http://www.google.com/landing/now/.Google Scholar
- S. Govindan, A. Sivasubramaniam, and B. Urgaonkar. Benefits and Limitations of Tapping into Stored Energy for Datacenters. In ISCA, 2011. Google ScholarDigital Library
- H. He, R. Xiong, X. Zhang, F. Sun, and J. Fan. State-of-Charge Estimation of the Lithium-Ion Battery Using and Adaptive Extended Kalman Filter Based on an Improved Thevenin Model. IEEE Trans. Vehicular Technology, 60(4):1461--1469, 2011.Google ScholarCross Ref
- B. D. Higgins, J. Flinn, T. J. Giuli, B. Noble, C. Peplin, and D. Watson. Informed Mobile Prefetching. In Proc. 10th ACM Mobisys, Low Wood Bay, United Kingdom, June 2012. Google ScholarDigital Library
- iFixit Galaxy Tab 2 10.1 Teardown: 25.9 Wh battery with two cells (Step 10). https://www.ifixit.com/Teardown/Samsung+Galaxy+Note+10.1+Teardown/10144.Google Scholar
- iFixit iPad Air 2 Teardown: 32.9 Wh battery with cells (Step 17). https://www.ifixit.com/Teardown/iPad+Air+2+Teardown/30592.Google Scholar
- iFixit Surface Pro 3 Teardown: 42.4 Wh battery with four cells (Step 13). https://www.ifixit.com/Teardown/Microsoft+Surface+Pro+3+Teardown/26595.Google Scholar
- Intel Active CPU Power Levels. http://www.intel.com/content/dam/www/public/us/en/documents/presentation/advancing-moores-law-in-2014-presentation.pdf.Google Scholar
- iPhone Battery Case. http://www.mophie.com/shop/iphone-5/juice-pack-helium-iphone-5.Google Scholar
- LTSpice: Linear Technologies Simulator Program with Integrated Circuit Emphasis.Google Scholar
- Maccor 4200 Battery Testing Equipment. http://www.maccor.com/Products/Model4200.aspx.Google Scholar
- Maxim Fuel Gague for Mobile Devices. http://para.maximintegrated.com/en/results.mvp?fam=batt_stat&295=Fuel%26nbsp%3BGauge&1379=ModelGauge.Google Scholar
- A. P. Miettinen and J. K. Nurminen. Energy Efficiency of Mobile Clients in Cloud Computing. In Proc. 2nd USENIX HotCloud, Boston, MA, June 2010. Google ScholarDigital Library
- R. Mittal, A. Kansal, and R. Chandra. Empowering Developers to Estimate App Energy Consumption. In Proc. 18th ACM MobiCom, Istanbul, Turkey, Aug. 2012. Google ScholarDigital Library
- A. Pathak, Y. C. Hu, and M. Zhang. Where is the energy spent inside my app?: Fine Grained Energy Accounting on Smartphones. In Proc. EUROSYS, Bern, Switzerland, Apr. 2012. Google ScholarDigital Library
- A. Pathak, Y. C. Hu, M. Zhang, P. Bahl, and Y.-M. Wang. Fine-Grained Power Modeling for Smartphones using System Call Tracing. In Proc. 6th ACM EUROSYS, Salzburg, Austria, Apr. 2011. Google ScholarDigital Library
- F. Qian, Z. Wang, A. Gerber, Z. M. Mao, S. Sen, and O. Spatschek. Profiling Resource Usage for Mobile Applications: a Cross-layer Approach. In Proc. 9th ACM MobiSys, Washington, DC, June 2011. Google ScholarDigital Library
- Qualcomm Quick Charge. https://www.qualcomm.com/products/snapdragon/quick-charge.Google Scholar
- A. Roy, S. M. Rumble, R. Stutsman, P. Levis, D. Mazieres, and N. Zeldovich. Energy Management in Mobile Devices with Cinder Operating System. In Proc. 6th ACM EUROSYS, Salzburg, Austria, Apr. 2011. Google ScholarDigital Library
- Samsung Galaxy Gear Specs. http://www.samsung.com/us/mobile/wearable-tech/SM-V7000ZKAXAR.Google Scholar
- Samsung Gear Live and Gear 2. http://www.gizmag.com/samsung-gear-live-vs-gear-2-smartwatch-comparison/32775/.Google Scholar
- A. Shye, B. Scholbrock, and G. Memik. Into the wild: Studying real user activity patterns to guide power optimizations for mobile architectures. In Proc. 42nd IEEE MICRO, New York, NY, Dec. 2009. Google ScholarDigital Library
- Siri Personal Assistant. https://www.apple.com/ios/siri/.Google Scholar
- Surface Power Cover. http://www.microsoft.com/surface/en-us/support/hardware-and-drivers/power-cover.Google Scholar
- Texas Instruments Fuel Guages for Mobile Devices. http://www.ti.com/lsds/ti/power-management/battery-fuel-gauge-products.page#p1152=SingleCell&p338=Li-Ion/Li-Polymer&p199=I2C&o4=ACTIVE&p626max=2000;29000&p626min=100;100&p1960=&p2192=&p2954=DSBGA.Google Scholar
- N. Thiagarajan, G. Aggarwal, A. Nicoara, D. Boneh, and J. P. Signh. Who Killed My Battery: Analyzing Mobile Browser Energy Consumption. In Proc. WWW, Lyon, France, Apr. 2012. Google ScholarDigital Library
- D. Wang, C. Ren, A. Sivasubramaniam, B. Urgaonkar, and H. Fathy. Energy storage in datacenters: what, where, and how much? In ACM SIGMETRICS, 2012. Google ScholarDigital Library
- Y. Wang, J. Lin, M. Annavaram, Q. A. Jacobson, J. Hong, B. Krishnamachari, and N. Sadeh-Koniecpol. A Framework for Energy Efficient Mobile Sensing for Automatic Human State Recognition. In Proc. 7th ACM Mobisys, Krakow, Poland, June 2009. Google ScholarDigital Library
- F. Xu, Y. Liu, T. Moscibroda, R. Chandra, L. Jin, Y. Zhang, and Q. Li. Optimizing Background Email Sync on Smartphones. In Proc. 11th ACM MobiSys, Taipei, Taiwan, June 2013. Google ScholarDigital Library
- C. Yoon, D. Kim, W. Jung, C. Kang, and H. Cha. AppScope: Application Energy Metering Framework for Android Smartphones using Kernel Activity Monitoring. In Proc. USENIX ATC, Boston, MA, June 2012. Google ScholarDigital Library
- L. Zhang, B. Tiwana, Z. Qian, Z. Wang, R. P. Dick, Z. M. Mao, and L. Yang. Accurate online power estimation and automatic battery behavior based power model generation for smartphones. In Proc. 8th IEEE/ACM/IFIP CODES+ISSS, Taipei, Taiwan, 2010. Google ScholarDigital Library
Index Terms
- Software defined batteries
Recommendations
Drowsy power management
SOSP '15: Proceedings of the 25th Symposium on Operating Systems PrinciplesPortable computing devices have fast multi-core processors, large memories, and many on-board sensors and radio interfaces, but are often limited by their energy consumption. Traditional power management subsystems have been extended for smartphones and ...
Reconfigurable Battery Systems: A Survey on Hardware Architecture and Research Challenges
In a reconfigurable battery pack, the connections among cells can be changed during operation to form different configurations. This can lead a battery, a passive two-terminal device, to a smart battery that can reconfigure itself according to the ...
Software-defined batteries
Different battery chemistries perform better on different axes, such as energy density, cost, peak power, recharge time, longevity, and efficiency. Mobile system designers are constrained by existing technology, and are forced to select a single ...
Comments