Accurate on-board capacity estimation is of critical importance in lithium-ion battery applications. Battery charging discharging often occurs under a constant current load, and hence voltage versus time measurements under this condition may be accessible in practice. This paper presents a data-driven diagnostic technique, Gaussian process regression for in situ capacity estimation (GP-ICE), which estimates battery capacity using voltage measurements over short periods of galvanostatic operation. Unlike previous works, GP-ICE does not rely on interpreting the voltage–time data as incremental capacity (IC) or differential voltage (DV) curves. This overcomes the need to differentiate the voltage–time data (a process that amplifies measurement noise), and the requirement that the range of voltage measurements encompasses the peaks in the IC DV curves. GP-ICE is applied to two datasets, consisting of 8 and 20 cells, respectively. In each case, within certain voltage ranges, as little as 10 s of galvanostatic operation enables capacity estimates with approximately 2 –3 root-mean-squared error (RMSE).
Lastly, we briefly compare the present approach with other recent studies related to feature extraction of online measurements for battery SOH estimation. We consider here only the most relevant studies; the reader is referred to the review studies @cite_39 @cite_32 @cite_11 for details of other approaches.
@cite_11 @cite_32 @cite_39
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Summary Estimating the dynamic status parameters of a battery, such as its state of health (SoH) and remaining useful life (RUL), is still a very difficult and complex task. In this paper we perform a structured review of the most relevant state of the art models, algorithms and commercial devices employed in the estimation of the SoH RUL battery performance figures, in the context of embedded applications. The models and estimation techniques are thoroughly classified and, for each taxonomy class, a presentation of the working principles is made. A comprehensive set of metrics is then introduced for the evaluation of the SoH RUL estimation techniques from the perspective of their implementation and operation efficiency in embedded systems. These algorithms are then analyzed and discussed in a comparative manner, with concrete figures and results. The capability and the performance of the different types of off-the-shelf fuel gauges to estimate the battery SoH RUL parameters are also evaluated in this paper. Copyright © 2016 John Wiley & Sons, Ltd. McDonald's (1970) generalization of Tucker's (1958) inter-battery factor analysis model to multiple batteries is considered. The identification of parameters is examined and an iterative algorithm for obtaining maximum-likelihood estimates is provided. Consideration of the relationship between inter-battery factor analysis and canonical correlation analysis in the case of two batteries suggests a generalization of canonical coefficients to the situation where there are several batteries. Examples of the application of the procedure are given. Abstract Lithium-ion batteries are used as the main power source in many electronic and electrical devices. In particular, with the growth in battery-powered electric vehicle development, the lithium-ion battery plays a critical role in the reliability of vehicle systems. In order to provide timely maintenance and replacement of battery systems, it is necessary to develop a reliable and accurate battery health diagnostic that takes a prognostic approach. Therefore, this paper focuses on two main methods to determine a battery's health: (1) Battery State-of-Health (SOH) monitoring and (2) Remaining Useful Life (RUL) prediction. Both of these are calculated by using a filter algorithm known as the Support Vector Regression-Particle Filter (SVR-PF). Models for battery SOH monitoring based on SVR-PF are developed with novel capacity degradation parameters introduced to determine battery health in real time. Moreover, the RUL prediction model is proposed, which is able to provide the RUL value and update the RUL probability distribution to the End-of-Life cycle. Results for both methods are presented, showing that the proposed SOH monitoring and RUL prediction methods have good performance and that the SVR-PF has better monitoring and prediction capability than the standard particle filter (PF). The evolution of battery technology is not being able to keep up with the increasing performance demand of mobile embedded systems. Therefore, battery life has become an important design constraint. As battery-operated embedded devices are deployed in mission critical systems, designers should ensure that the energy constraints are satisfied in addition to the timing constraints  the battery should not drain out before a critical task completes execution. Giving these guarantees requires the knowledge of the worst-case execution time and energy of a task. Significant progress has been made in estimating the worst-case execution time through static analysis. In contrast, existing energy estimation techniques use average-case execution profile of a program and as such cannot guarantee the satisfiability of energy constraints. In this paper, we present a static analysis technique to estimate the worst-case energy consumption of a task on complex micro-architectures. Estimating a bound on energy is non-trivial as it is unsafe to assume any direct correlation with the bound on execution time. Experimental evaluation with a number of benchmark programs indicates the accuracy of our worst-case energy consumption estimates.