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. The functionality and reliability of Li-ion batteries as major energy storage devices have received more and more attention from a wide spectrum of stakeholders, including federal state policymakers, business leaders, technical researchers, environmental groups and the general public. Failures of Li-ion battery not only result in serious inconvenience and enormous replacement repair costs, but also risk catastrophic consequences such as explosion due to overheating and short circuiting. In order to prevent severe failures from occurring, and to optimize Li-ion battery maintenance schedules, breakthroughs in prognostics and health monitoring of Li-ion batteries, with an emphasis on fault detection, correction and remaining-useful-life prediction, must be achieved. This paper reviews various aspects of recent research and developments in Li-ion battery prognostics and health monitoring, and summarizes the techniques, algorithms and models used for state-of-charge (SOC) estimation, current voltage estimation, capacity estimation and remaining-useful-life (RUL) prediction. Abstract This work provides an overview of available methods and algorithms for on-board capacity estimation of lithium-ion batteries. An accurate state estimation for battery management systems in electric vehicles and hybrid electric vehicles is becoming more essential due to the increasing attention paid to safety and lifetime issues. Different approaches for the estimation of State-of-Charge, State-of-Health and State-of-Function are discussed and analyzed by many authors and researchers in the past. On-board estimation of capacity in large lithium-ion battery packs is definitely one of the most crucial challenges of battery monitoring in the aforementioned vehicles. This is mostly due to high dynamic operation and conditions far from those used in laboratory environments as well as the large variation in aging behavior of each cell in the battery pack. Accurate capacity estimation allows an accurate driving range prediction and accurate calculation of a battery's maximum energy storage capability in a vehicle. At the same time it acts as an indicator for battery State-of-Health and Remaining Useful Lifetime estimation.