State of charge

About: State of charge is a research topic. Over the lifetime, 12013 publications have been published within this topic receiving 201419 citations. The topic is also known as: SoC & SOC.

Energy efficient battery management

Abstract: A challenging aspect of mobile communications consists in exploring ways in which the available run time of terminals can be maximized. We present a detailed electrochemical battery model and a simple stochastic model that captures the fundamental behavior of the battery. The stochastic model is then matched to the electrochemical model and used to investigate battery management techniques that may improve the energy efficiency of radio communication devices. We consider an array of electrochemical cells. Through simple scheduling algorithms, the discharge from each cell is properly shaped to optimize the charge recovery mechanism, without introducing any additional delay in supplying the required power. Then, a battery management scheme, which exploits knowledge of the cells' state of charge, is implemented to achieve a further improvement in the battery performance. In this case, the discharge demand may be delayed. Results indicate that the proposed battery management techniques improve system performance no matter which parameters values are chosen to characterize the cells' behavior.

State-of-the-art of battery state-of-charge determination

Abstract: 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.

State-of-Charge Estimation and State-of-Health Prediction of a Li-Ion Degraded Battery Based on an EKF Combined With a Per-Unit System

Abstract: This paper describes the application of an extended Kalman filter (EKF) combined with a per-unit (p.u.) system to the identification of suitable battery model parameters for the high-accuracy state-of-charge (SOC) estimation and state-of-health (SOH) prediction of a Li-Ion degraded battery. Variances in electrochemical characteristics among Li-Ion batteries caused by aging differences result in erroneous SOC estimation and SOH prediction when using the existing EKF algorithm. To apply the battery model parameters varied by the aging effect, based on the p.u. system, the absolute values of the parameters in the equivalent circuit model in addition to the discharging/charging voltage and current are converted into dimensionless values relative to a set of base value. The converted values are applied to dynamic and measurement models in the EKF algorithm. In particular, based on two methods such as direct current internal resistance measurement and the statistical analysis of voltage pattern, each diffusion resistance (RDiff) can be measured and used for offline and online SOC estimations, respectively. All SOC estimates are within ±5% of the values estimated by ampere-hour counting. Moreover, it is shown that RDiff is more sensitive than other model parameters under identical experimental conditions and, hence, implementable for SOH prediction.