VRLA battery state of health estimation based on charging time

Battery state of health (SoH) is an important parameter of the battery’s ability to store and deliver electrical energy. Various methods have been so far developed to calculate the battery SoH, such as through the calculation of battery impedance or battery capacity using Kalman Filter, Fuzzy theory, Probabilistic Neural Network, adaptive hybrid battery model, and Double Unscented Kalman Filtering (D-UKF) algorithm. This paper proposes an approach to estimate the value of battery SoH based on the charging time measurement. The results of observation and measurements showed that a new and used batteries would indicate different charging times. Unhealthy battery tends to have faster charging and discharging time. The undertaken analysis has been focused on finding out the relationship between the battery SoH and the charging time range. To validate the results of this proposed approach, the use of battery capacity method has been considered as comparison. It can be concluded that there is a strong correlation between the two discussed SoH estimation methods, confirming that the proposed method is feasible as an alternative SoH estimation method to the widely known battery capacity method. The correlation between the charging-disharging times of healthy and unhealthy batteries is very prospective to develop a battery charger in the future with a prime advantage of not requiring any sensor for the data acquisition

The Effect of Voltage Dataset Selection on the Accuracy of Entropy-Based Capacity Estimation Methods for Lithium-Ion Batteries

It is important to accurately estimate the capacity of the battery in order to extend the service life of the battery and ensure the reliable operation of the battery energy storage system. As entropy can quantify the regularity of a dataset, it can serve as a feature to estimate the capacity of batteries. In order to analyze the effect of voltage dataset selection on the accuracy of entropy-based estimation methods, six voltage datasets were collected, considering the current direction (i.e., charging or discharging) and the state of charge level. Furthermore, three kinds of entropies (approximate entropy, sample entropy, and multiscale entropy) were introduced, and the relationship between the entropies and the battery capacity was established by using first-order polynomial fitting. Finally, the interaction between the test conditions, entropy features, and estimation accuracy was analyzed. Moreover, the results can be used to select the correct voltage dataset and improve the estimation accuracy

Predicting the Batteries' State of Health in Wireless Sensor Networks Applications

[EN] The lifetime of wireless sensor networks deployments depends strongly on the nodes battery state of health (SoH). It is important to detect promptly those motes whose batteries are affected and degraded by ageing, environmental conditions, failures, etc. There are several parameters that can provide significant information of the battery SoH, such as the number of charge/discharge cycles, the internal resistance, voltage, drained current, temperature, etc. The combination of these parameters can be used to generate analytical models capable of predicting the battery SoH. The generation of these models needs a previous process to collect dense data traces with sampled values of the battery parameters during a large number of discharge cycles under different operating conditions. The collected data allow the development of mathematical models that can predict the battery SoH. These models are required to be simple because they must be executed in motes with low computational capabilities. The paper shows the complete process of acquiring the training data, the models generation and its experimental validation using rechargeable batteries connected to Telosb motes. The obtained results provide significant insight of the battery SoH at different temperatures and charge/discharge cycles.This work was supported in part by the Spanish MINECO under Grant BIA2016-76957-C3-1-R and in part by the I+D+i Program of the Generalitat Valenciana, Spain, under Grant AICO/2016/046.Lajara Vizcaino, JR.; Perez Solano, JJ.; Pelegrí Sebastiá, J. (2018). Predicting the Batteries' State of Health in Wireless Sensor Networks Applications. IEEE Transactions on Industrial Electronics. 65(11):8936-8945. https://doi.org/10.1109/TIE.2018.2808925S89368945651

Online voltage prediction using gaussian process regression for fault-tolerant photovoltaic standalone applications

This paper presents a fault detection system for photovoltaic standalone applications based on Gaussian Process Regression (GPR). The installation is a communication repeater from the Confederacion Hidrografica del Ebro (CHE), public institution which manages the hydrographic system of Aragon, Spain. Therefore, fault-tolerance is a mandatory requirement, complex to fulfill since it depends on the meteorology, the state of the batteries and the power demand. To solve it, we propose an online voltage prediction solution where GPR is applied in a real and large dataset of two years to predict the behavior of the installation up to 48 hour. The dataset captures electrical and thermal measures of the lead-acid batteries which sustain the installation. In particular, the crucial aspect to avoid failures is to determine the voltage at the end of the night, so different GPR methods are studied. Firstly, the photovoltaic standalone installation is described, along with the dataset. Then, there is an overview of GPR, emphasizing in the key aspects to deal with real and large datasets. Besides, three online recursive multistep GPR model alternatives are tailored, justifying the selection of the hyperparameters: Regular GPR, Sparse GPR and Multiple Experts (ME) GPR. An exhaustive assessment is performed, validating the results with those obtained by Long Short-Term Memory (LSTM) and Nonlinear Autoregressive Exogenous Model (NARX) networks. A maximum error of 127 mV and 308 mV at the end of the night with Sparse and ME, respectively, corroborates GPR as a promising tool

The effect of cell-to-cell variations and thermal gradients on the performance and degradation of lithium-ion battery packs

The performance of lithium-ion battery packs are often extrapolated from single cell performance however uneven currents in parallel strings due to cell-to-cell variations, thermal gradients and/or cell interconnects can reduce the overall performance of a large scale lithium-ion battery pack. In this work, we investigate the performance implications caused by these factors by simulating six parallel connected batteries based on a thermally coupled single particle model with the solid electrolyte interphase growth degradation mechanism modelled. Experimentally validated simulations show that cells closest to the load points of a pack experience higher currents than cells further away due to uneven overpotentials caused by the interconnects. When a cell with a four times greater internal impedance was placed in the location with the higher currents this actually helped to equalise the cell-to-cell current distribution, however if this was placed at a location furthest from the load point this would cause a ~6% reduction in accessible energy at 1.5 C. The influence of thermal gradients can further affect this current heterogeneity leading to accelerated aging. Simulations show that in all cases, cells degrade at different rates in a pack due to the uneven currents, with this being amplified by thermal gradients. In the presented work a 5.2% increase in degradation rate, from -7.71 mWh/cycle (isothermal) to - 8.11 mWh/cycle (non-isothermal) can be observed. Therefore, the insights from this paper highlight the highly coupled nature of battery pack performance and can inform designs for higher performance and longer lasting battery packs

Comparative Study of Online Open Circuit Voltage Estimation Techniques for State of Charge Estimation of Lithium-Ion Batteries

Online estimation techniques are extensively used to determine the parameters of various uncertain dynamic systems. In this paper, online estimation of the open-circuit voltage (OCV) of lithium-ion batteries is proposed by two different adaptive filtering methods (i.e., recursive least square, RLS, and least mean square, LMS), along with an adaptive observer. The proposed techniques use the battery’s terminal voltage and current to estimate the OCV, which is correlated to the state of charge (SOC). Experimental results highlight the effectiveness of the proposed methods in online estimation at different charge/discharge conditions and temperatures. The comparative study illustrates the advantages and limitations of each online estimation method

Optimizing machine learning for agricultural productivity: A novel approach with RScv and remote sensing data over Europe

CONTEXT: Accurate estimating of crop yield is crucial for developing effective global food security strategies which can lead to reduce of hunger and more sustainable development. However, predicting crop yields is a complex task as it requires frequent monitoring of many weather and socio-economic factors over an extended period. Satellite remote sensing products have become a reliable source for climate-based variables. They are easier to obtain and provide detailed spatial and temporal coverage. OBJECTIVE: The aim of this study is to assess the effectiveness of implement a novel optimization algorithm, called Randomized Search cross validation (RScv), on various machine learning algorithms and measure the prediction accuracy enhancement. METHODS: Annual yields of four crops (Barley, Oats, Rye, and Wheat) were predicted across 20 European countries for 20 years (2000–2019). Two NASA missions, namely GPCP and GLDAS satellites, provided us with climate- and soil-based input variables. Those variables were employed as the input of four ensemble Machine Learning (ML) algorithms (Ada-Boost (AB), Gradient Boost (GB), Random Forest (RF) and Extra Tree (ET)) which are faster and more adoptable compare to classic AI algorithms. RESULTS AND CONCLUSIONS: Main results show that applying RScv improves the prediction ability of all ML models over the four crops. In particular, the RScv-AB reaches the overall highest accuracy for predicting yields (R2max = 0.9). Spatial evaluation of predicting errors depicts that the proposed models were more shifted toward underestimation. An uncertainty analysis was also carried out which shows that applying ML algorithms creates higher and lowers uncertainty in Barley and Wheat respectively. SIGNIFICANCE: Considering the robustness of the optimised ML models and the global coverage of remote sensing data, our current methodology demonstrates great transferability and can be applied in other regions across the globe with higher temporal extents. In addition, this tool could be beneficial to decision makers in various sectors to improve the water allocations, deal with climate change effects and keep sustainable agricultural development.Antonio Jodar-Abellan acknowledges financial support received form the Margarita Salas Postdoc Spanish Program