A scheduling model for reduced CPU energy
FOCS '95 Proceedings of the 36th Annual Symposium on Foundations of Computer Science
Battery Life Estimation of Mobile Embedded Systems
VLSID '01 Proceedings of the The 14th International Conference on VLSI Design (VLSID '01)
Energy management for battery-powered embedded systems
ACM Transactions on Embedded Computing Systems (TECS)
Balancing batteries, power, and performance: system issues in cpu speed-setting for mobile computing
Balancing batteries, power, and performance: system issues in cpu speed-setting for mobile computing
Real Time Dynamic Voltage Scaling For Embedded Systems
VLSID '04 Proceedings of the 17th International Conference on VLSI Design
Battery Model for Embedded Systems
VLSID '05 Proceedings of the 18th International Conference on VLSI Design held jointly with 4th International Conference on Embedded Systems Design
Dynamic node activation in networks of rechargeable sensors
IEEE/ACM Transactions on Networking (TON)
Optimal Dynamic Voltage Scaling in Energy-Limited Nonpreemptive Systems with Real-Time Constraints
IEEE Transactions on Mobile Computing
Energy efficient battery management
IEEE Journal on Selected Areas in Communications
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Motivated by the increasing dependence of many systems on battery energy, we study the problem of optimally controlling how to discharge and recharge a non-ideal battery so as to maximize the work it can perform over a given time period and still maintain a desired final energy level. Modeling a battery as a dynamic system, we adopt a Kinetic Battery Model (KBM) and formulate a finite-horizon optimal control problem when recharging is always feasible under the constraint that discharging and recharging cannot occur at the same time. The solution is shown to be of bang-bang type with the property that the battery is always in recharging mode during the last part of the interval. When the length of the time horizon exceeds a critical value, we also show that the optimal policy includes chattering. Numerical results are included to illustrate our analysis. We then extend the problem to settings where recharging is only occasionally feasible and show that it can be reduced to a nonlinear optimization problem which can be solved at least numerically.