Accuracy Of Capacity Estimation Research Articles

Capacity estimation for lithium-ion battery via a novel health indicator extracted from partial constant voltage charging curve

This paper develops a capacity estimation method via a novel health indicator (HI) extracted from partial constant voltage (CV) charging curve. Firstly, the CV charging curve is analyzed based on the Thevenin model, whose results indicate that the CV charging curves under different battery aging states can be theoretically described by the same functional form. Secondly, with CV charging curve analysis results, regarding the CV charging curve at the first cycle as original curve, and those at other cycles as fitted curves, the coefficient of determination between those two kinds of curves is computed through the early-stage CV charging data and innovatively taken as HI for further capacity estimation. Meanwhile, considering that the early-stage CV charging data may still be insufficient in real application, a simple subsequent current prediction method is proposed to ease HI extraction in the case of insufficient data based on the first data point of CV charging curve. Thirdly, a moving window-based HI processing method is developed to mitigate the negative influence of the possible current measurement disturbances on capacity estimation accuracy. Finally, the proposed method is verified through Underwriters Laboratories Inc. - Purdue University battery aging dataset, whose results demonstrate that both mean absolute error and root mean squared error of capacity estimation results achieved by the model established by only 12 pairs of data points in the premise of appropriately selected original curve can be roughly controlled less than 1% and 1.5% in the case of sufficient data and insufficient data, respectively.

A Data-Driven LiFePO4 Battery Capacity Estimation Method Based on Cloud Charging Data from Electric Vehicles

The accuracy of capacity estimation is of great importance to the safe, efficient, and reliable operation of battery systems. In recent years, data-driven methods have emerged as promising alternatives to capacity estimation due to higher estimation accuracy. Despite significant progress, data-driven methods are mainly developed by experimental data under well-controlled charge–discharge processes, which are seldom available for practical battery health monitoring under realistic conditions due to uncertainties in environmental and operational conditions. In this paper, a novel method to estimate the capacity of large-format LiFePO4 batteries based on real data from electric vehicles is proposed. A comprehensive dataset consisting of 85 vehicles that has been running for around one year under diverse nominal conditions derived from a cloud platform is generated. A classification and aggregation capacity prediction method is developed, combining a battery aging experiment with big data analysis on cloud data. Based on degradation mechanisms, IC curve features are extracted, and a linear regression model is established to realize high-precision estimation for slow-charging data with constant-current charging. The selected features are highly correlated with capacity (Pearson correlation coefficient < 0.85 for all vehicles), and the MSE of the capacity estimation results is less than 1 Ah. On the basis of protocol analysis and mechanism studies, a feature set including internal resistance, temperature, and statistical characteristics of the voltage curve is constructed, and a neural network (NN) model is established for multi-stage variable-current fast-charging data. Finally, the above two models are integrated to achieve capacity prediction under complex and changeable realistic working conditions, and the relative error of the capacity estimation method is less than 0.8%. An aging experiment using the battery, which is the same as those equipped in the vehicles in the dataset, is carried out to verify the methods. To the best of the authors’ knowledge, our study is the first to verify a capacity estimation model derived from field data using an aging experiment of the same type of battery.

An ensemble learning prognostic method for capacity estimation of lithium-ion batteries based on the V-IOWGA operator

Capacity estimation is crucial for assessing the health statuses of lithium-ion batteries to develop better battery usage and maintenance strategies. However, it is difficult to satisfy multiple application situations in which each individual prognostic method has its own particular preconditions and application limitations. Thus, in this paper, an ensemble prognostic framework is proposed to integrate several individual prognostic methods to achieve better capacity estimation accuracy. In the proposed framework, a measurement- and calculation-based combined feature extraction method is first applied to the battery charging phase to better capture the extracted features that are related to the health statuses of lithium-ion batteries. Then, a novel validation dataset-based induced ordered weighted geometric averaging (V-IOWGA) operator is proposed to realize the time-varying weight allocation of each individual prognostic method to solve issue by which the performances of different prognostic algorithms vary in different phases. The advantages of the proposed ensemble model are verified on lithium-ion battery datasets from NASA PCoE and the University of Maryland CALCE Laboratory, and its prediction accuracy is better than that of other individual prognostic models. In addition, comparison experiments involving three types of ensemble models validate the superiority of the proposed model.

Online Capacity Estimation for Lithium-Ion Batteries Based on Semi-Supervised Convolutional Neural Network

Accurate capacity estimation can ensure the safe and reliable operation of lithium-ion batteries in practical applications. Recently, deep learning-based capacity estimation methods have demonstrated impressive advances. However, such methods suffer from limited labeled data for training, i.e., the capacity ground-truth of lithium-ion batteries. A capacity estimation method is proposed based on a semi-supervised convolutional neural network (SS-CNN). This method can automatically extract features from battery partial-charge information for capacity estimation. Furthermore, a semi-supervised training strategy is developed to take advantage of the extra unlabeled sample, which can improve the generalization of the model and the accuracy of capacity estimation even in the presence of limited labeled data. Compared with artificial neural networks and convolutional neural networks, the proposed method is demonstrated to improve capacity estimation accuracy.

Lithium-ion battery capacity estimation based on open circuit voltage identification using the iteratively reweighted least squares at different aging levels

In traditional incremental SOC (stage of charge)-capacity method, errors of the selected SOC points inevitably lead to inaccurate capacity estimation. In this paper, a capacity estimation method based on OCV (open circuit voltage) identification and IRLS (iteratively reweighted least squares) algorithm method is proposed. Firstly, the accuracy and complexity of three parameter identification schemes of first-order RC model at different aging levels are studied with the forgetting factor recursive least squares algorithm. Through the online identified OCV by the optimal scheme, the full interval SOC estimation can be obtained indirectly. Finally, the capacity is estimated using the full interval SOC estimation with IRLS algorithm, which avoids the randomness of the SOC point selection, and solves the problem of possible outliers in the SOC estimation or the unknown variance of the random error of the SOC gain. Through the designed aging experiment, the capacity estimation accuracy is verified under different life conditions. The results show that the estimation accuracy of this method is high and the error does not exceed 2.5%.

An Improved Recursive Total Least Squares Estimation of Capacity for Electric Vehicle Lithium-Iron Phosphate Batteries

A battery’s capacity is an important indicator of its state of health and determines the maximum cruising range of electric vehicles. It is also a crucial piece of information for helping improve state of charge (SOC) estimation, health prognosis, and other related tasks in the battery management system (BMS). In this paper, we propose an improved recursive total least squares approach to online capacity estimation, which is based on the constrained Rayleigh quotient in terms of battery capacity. This approach accounts for errors in both the SOC and accumulated current measurements not traditionally considered in the battery capacity model to give an unbiased estimation. Moreover, the forgetting factor, updated by minimizing the Rayleigh quotient of the capacity estimation model, is applied to track the changes in the model and get a more precise estimation of the capacity. Finally, the performance of the proposed algorithm is validated via simulation and experimental studies on lithium-iron phosphate batteries. The estimation results show that the proposed algorithm improves capacity estimation accuracy.

Online available capacity prediction and state of charge estimation based on advanced data-driven algorithms for lithium iron phosphate battery

The key technology of a battery management system is to online estimate the battery states accurately and robustly. For lithium iron phosphate battery, the relationship between state of charge and open circuit voltage has a plateau region which limits the estimation accuracy of voltage-based algorithms. The open circuit voltage hysteresis requires advanced online identification algorithms to cope with the strong nonlinear battery model. The available capacity, as a crucial parameter, contributes to the state of charge and state of health estimation of battery, but it is difficult to predict due to comprehensive influence by temperature, aging and current rates. Aim at above problems, the ampere-hour counting with current correction and the dual adaptive extended Kalman filter algorithms are combined to estimate model parameters and state of charge. This combination presents the advantages of less computation burden and more robustness. Considering the influence of temperature and degradation, the data-driven algorithm namely least squares support vector machine is implemented to predict the available capacity. The state estimation and capacity prediction methods are coupled to improve the estimation accuracy at different temperatures among the lifetime of battery. The experiment results verify the proposed methods have excellent state and available capacity estimation accuracy.

Battery Capacity Fading Estimation Using a Force-Based Incremental Capacity Analysis

Traditionally health monitoring techniques in lithium-ion batteries rely on voltage and current measurements. A novel method of using a mechanical rather than electrical signal in the incremental capacity analysis (ICA) method is introduced in this paper. This method derives the incremental capacity curves based on measured force (ICF) instead of voltage (ICV). The force is measured on the surface of a cell under compression in a fixture that replicates a battery pack assembly and preloading. The analysis is performed on data collected from cycling encased prismatic Lithium-ion Nickel-Manganese-Cobalt Oxide (NMC) cells. For the NMC chemistry, the ICF method can complement or replace the ICV method for the following reasons. The identified ICV peaks are centered around 40% of state of charge (SOC) while the peaks of the ICF method are centered around 70% of SOC indicating that the ICF can be used more often because it is more likely that an electric vehicle (EV) or a plug-in hybrid electric vehicle (PHEV) will traverse the 70% SOC range than the 40% SOC. In addition the Signal to Noise ratio (SNR) of the force signal is four times larger than the voltage signal using laboratory grade sensors. The proposed ICF method is shown to achieve 0.42% accuracy in capacity estimation during a low C-rate constant current discharge. Future work will investigate the application of the capacity estimation technique under charging and operation under high C-rates by addressing the transient behavior of force so that an online methodology for capacity estimation is developed.

Analytic Bound on Accuracy of Battery State and Parameter Estimation

Methods for battery state and parameter estimation have been widely investigated, while the achievable accuracy of the estimation remains a critical but somehow overlooked topic. In this paper, the analytic bounds on the accuracy of battery state and parameter estimation accounting for voltage measurement noises are derived based on the Fisher information matrix and Cramer-Rao bound analysis. The state and parameters under discussion include the state of charge, capacity and (ohmic) resistance. The estimation accuracy is influenced by the information contained in the data set used for estimation. It is found that the main contributing factors to the accuracy of SOC estimation are the slope of the OCV curve and number of data points, while the accuracy of capacity estimation is affected by both OCV slope and SOC variation, and that of resistance estimation depends heavily on the current magnitude. The analytic bounds are derived for both standalone estimation, where only one state/parameter is estimated, and combined estimation where they are estimated together. The loss of accuracy in combined estimation compared to standalone estimation is usually expected. However, when the current excitation satisfies certain patterns, such loss can be avoided. The conclusions can be used as guidelines for offline experiment design as well as online evaluation of the accuracy of adaptive state and parameter estimation.

Prediction of Capacity for Roundabouts Based on Percentages of Trucks in Entry and Circulating Flows

The objective of this study was to estimate capacity at roundabouts by developing a method to adjust gap acceptance parameters for trucks. Because drivers' gap acceptance behavior is affected not only by trucks in the entry flow but also by trucks in the circulating flow, critical headways were separately estimated for various combinations of vehicle types in the circulating flow at 11 roundabouts in Ontario, Canada; Vermont; and Wisconsin. Because the percentage of trucks was different for different entry legs, the critical headways and follow-up times were estimated at each leg separately. Variations in gap acceptance behavior were also observed at one of the 11 roundabouts for 13 consecutive days to evaluate the statistical significance of differences in behavior between two entry legs. The results showed that a new adjusted critical headway improved the accuracy of capacity estimation and that the critical headways were significantly different between the two legs with different percentages of trucks in the entry flow. The study provided insight into how to capture the effect of trucks on roundabout capacity.

Implementing the maximum likelihood methodology to measure a driver’s critical gap

Most of the capacity calculation procedures for two-way stop-controlled (TWSC) intersections are based on gap acceptance models. Critical gap is one of the major parameters for gap acceptance models. The accuracy of capacity estimation is mainly determined by the accuracy of the critical gap. This paper focuses on the implementation of the maximum likelihood technique to measure a driver’s critical gap using field data. A methodology to define gap events is proposed, so that the accepted gaps and maximum rejected gaps required by the maximum likelihood technique could be obtained. Specific issues regarding multi-lane situations and major street right turn movement are discussed. Special conditions observed during the research are addressed when the proposed method cannot be applied directly, such as the existence of a mid-block refuge area where minor street drivers can seek gaps in a two-stage process, pedestrian blockage, and downstream queue spill back. The proposed method was adopted in measuring critical gap under US conditions during a research project, described by Kyte et al. (1996). ©