From the early days of its discovery, humanity has depended on electricity, a phenomenon without which our technological advancements would not have been possible. With the increased need for mobility, people moved to portable power storage—first for wheeled applications, then for portable and finally nowadays wearable use. Several types of rechargeable battery systems, including those of lead–acid, nickel–cadmium, nickel–metal hydride, lithium ion and lithium-ion polymer exist in the market. The most important of them will be discussed in this review. Almost as long as rechargeable batteries have existed, systems able to give an indication about the state-of-charge (SoC) of a battery have been around. Several methods, including those of direct measurements, book-keeping and adaptive systems (Bergveld et al 2002 Battery Management Systems, Design by Modelling (Philips Research Book Series) vol 1 (Boston: Kluwer)) are known in the art for determining the SoC of a cell or battery of cells. An accurate SoC determination method and an understandable and reliable SoC display to the user will improve the performance and reliability, and will ultimately lengthen the lifetime of the battery. However, many examples of poor accuracy and reliability can be found in practice (Bergveld et al 2002, cited above). This review presents an overview on battery technology and the state-of-the-art of SoC methods. The goal of all the presented SoC indication methods is to design an SoC indication system capable of providing an accurate SoC indication under all realistic user conditions, including those of spread—in both battery and user behaviour, a large temperature and current range and ageing of the battery.

Li -ion is the most commonly used battery chemistry in portable applications nowadays. Accurate state-of-charge (SOC) and remaining run-time indication for portable devices is important for the user’s convenience and to prolong the lifetime of batteries. A new SOC indication system, combining the electromotive force (EMF) measurement during equilibrium and current measurement and integration during charge and discharge, has been developed and implemented in a laboratory setup. During discharge, apart from simple Coulomb counting, the effect of the overpotential is also considered. Mathematical models describing the EMF and the overpotential functions for a Li -ion battery have been developed. These models include a variety of parameters whose values depend on the determination method and experimental conditions. In this paper the battery measurement and modeling efforts are described. The method of implementing the battery model in an SOC indication system is also described. The aim is an SOC determination within 1% inaccuracy or better under all realistic user conditions, including a wide variety of load currents and a wide temperature range. The achieved results show the effectiveness of our novel approach for improving the accuracy of the SOC indication.

This paper describes the characterization of thin-film MEMS vibration energy harvesters based on aluminum nitride as piezoelectric material. A record output power of 85 μW is measured. The parasitic-damping and the energy-harvesting performances of unpackaged and packaged devices are investigated. Vacuum and atmospheric pressure levels are considered for the packaged devices. When dealing with packaged devices, it is found that vacuum packaging is essential for maximizing the output power. Therefore, a wafer-scale vacuum package process is developed. The energy harvesters are used to power a small prototype (1 cm 3 volume) of a wireless autonomous sensor system. The average power consumption of the whole system is less than 10 μW, and it is continuously provided by the vibration energy harvester.

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Abstract-The known methods of State-of-Charge (SoC) indication in portable applications are not accurate enough under all practical conditions. The method presented in this paper aims at designing and testing an SoC indication system capable of predicting the remaining capacity of the battery and the remaining run-time with an accuracy of 1 minute or better under all realistic user conditions, including a wide variety of load currents and a wide temperature range. The basis of the proposed algorithm is current measurement and integration during charge and discharge state and voltage measurement during equilibrium state. One of the main problems in designing an accurate SoC indication system is aging of the battery. A simple method of adapting the maximum battery capacity used in the system with the aging effects will be presented in this paper. A first set of experimental results shows the effectiveness of our novel approach for improving the accuracy of the SoC indication.

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An algorithm that combines adaptive system with Electro-Motive-Force (EMF) measurements during equilibrium and current measurement and integration during (dis)charge states has been implemented in a real-time SoC evaluation system. During the discharge state also the effect of the overpotential is considered. The overpotential model includes a variety of parameters that are changing during cycling of the battery. Adaptive methods for the battery maximum capacity and for the overpotential model parameters are presented in this paper. The final aim is to predict the SoC and the remaining run-time of a Li-ion battery within 1% inaccuracy under all realistic user conditions.