Kalman-variant estimators for state of charge in lithium-sulfur batteries

Lithium-sulfur batteries are now commercially available, offering high specific energy density, low production costs and high safety. However, there is no commercially-available battery management system for them, and there are no published methods for determining state of charge in situ. This paper describes a study to address this gap. The properties and behaviours of lithium-sulfur are briefly introduced, and the applicability of ‘standard’ lithium-ion state-of-charge estimation methods is explored. Open-circuit voltage methods and ‘Coulomb counting’ are found to have a poor fit for lithium-sulfur, and model-based methods, particularly recursive Bayesian filters, are identified as showing strong promise. Three recursive Bayesian filters are implemented: an extended Kalman filter (EKF), an unscented Kalman filter (UKF) and a particle filter (PF). These estimators are tested through practical experimentation, considering both a pulse-discharge test and a test based on the New European Driving Cycle (NEDC). Experimentation is carried out at a constant temperature, mirroring the environment expected in the authors' target automotive application. It is shown that the estimators, which are based on a relatively simple equivalent-circuit–network model, can deliver useful results. If the three estimators implemented, the unscented Kalman filter gives the most robust and accurate performance, with an acceptable computational effort

Comparative Evaluation of Investigation Methods for Estimating the Load-Dependent State of Charge and End of Discharge of a Multirotor UAV Battery

As the scope of multirotor unmanned aerial vehicle (UAV) applications increases, more attention is being paid to UAV energy requirements, which vary depending on the mission profile. To obtain accurate information about the UAV battery during flight, the idea of a digital twin including a battery state estimation model is promising. For battery state estimation, a Kalman filter combination is the preferred approach in the literature. Comparing different Kalman filters, the unscented Kalman filter has a more accurate estimation for nonlinear systems compared to the extended Kalman filter. In the application of UAV flight with load-dependent flight missions, the comparison of different Kalman filter estimation methods has not yet been researched. In order to evaluate the applicability of different state of charge estimation methods applied to different UAV flight missions, an extended Kalman filter, an unscented Kalman filter, and the Coulomb-counting method are implemented in this research and combined with an end of discharge estimation. To compare the estimation methods based on a delivery mission and a facade inspection mission, a parameter identification of the UAV battery is performed, and an equivalent circuit model is developed and combined with the estimation methods to estimate the battery state. The results of the investigation show that the unscented Kalman filter achieves more accurate state of charge estimation results than the extended Kalman filter, even in the field of UAV application. The results also show that the choice of estimation method is mainly influenced by the accuracy of the parameter identification process, while the dynamic load of a UAV mission has less impact. Contrarily, the end of discharge estimation does not correlate with the accuracy of the state of charge estimation, indicating that the end of discharge estimation is more dependent on the dynamic load

An adaptive working state iterative calculation method of the power battery by using the improved Kalman filtering algorithm and considering the relaxation effect.

The battery modeling and iterative state calculation in the battery management system is very important for the high-power lithium-ion battery packs, the accuracy of which affects its working performance and safety. An adaptive improved unscented Kalman filtering algorithm is developed to realize the iterative calculation process, aiming to overcome the rounding error in the numerical calculation treatment when it is used to estimate the nonlinear state value of the battery pack. As the sigma point is sampled in the unscented transform round from the unscented Kalman filter algorithm, an imaginary number appears that results in the working state estimation failure. In order to solve this problem, the decomposition is combined with the calculation process. Meanwhile, an adaptive noise covariance matching method is implied. Experiments show that the proposed method can guarantee the semi-positive and numerical stability of the state covariance, and the estimation accuracy can reach the third-order precision. The estimation error remains 1.60% under the drastic voltage and current change conditions, which can reduce the estimation error by 1.00% compared with the traditional method. It can provide a theoretical safety protection basis of the energy management for the lithium-ion battery pack

SOC Estimation for Electrical Vehicle lithium Batteries base on Simplified-spherical Un-scented Kalman Filtering

In order to develop electric vehicles, it is vital to be able to accurately estimate the state charge (SOC) of a lithium battery. To address the problem that the Extended Kalman Filter (EKF) algorithm leads to the Taylor expansion truncation of the higher-order sys-tem. In this paper, a system of state-space equations is established based on the sec-ond-order equivalent circuit model, and a simplified-sphere sample approach is used to improve the Unscented Kalman Filter (UKF) algorithm. The SOC estimation performance of the three algorithms is tested under constant current discharge, pulse dis-charge con-ditions, and UDC conditions, respectively. The simulation results show that Simpli-fied-spherical Unscented Kalman Filtering (SUKF) has smaller errors between SOC esti-mation and theoretical reference values than EKF and UKF. The SUKF is less computa-tionally intensive than UKF and has better timeliness in the onboard battery manage-ment system

A novel charged state prediction method of the lithium ion battery packs based on the composite equivalent modeling and improved splice Kalman filtering algorithm.

As the unscented Kalman filtering algorithm is sensitive to the battery model and susceptible to the uncertain noise interference, an improved iterate calculation method is proposed to improve the charged state prediction accuracy of the lithium ion battery packs by introducing a novel splice Kalman filtering algorithm with adaptive robust performance. The battery is modeled by composite equivalent modeling and its parameters are identified effectively by investigating the hybrid power pulse test. The sensitivity analysis is carried out for the model parameters to obtain the influence degree on the prediction effect of different factors, providing a basis of the adaptive battery characterization. Subsequently, its implementation process is carried out including model building and adaptive noise correction that are perceived by the iterate charged state calculation. Its experimental results are analyzed and compared with other algorithms through the physical tests. The polarization resistance is obtained as Rp = 16.66 mΩ and capacitance is identified as Cp = 13.71 kF. The ohm internal resistance is calculated as Ro = 68.71 mΩ and the charged state has a prediction error of 1.38% with good robustness effect, providing a foundational basis of the power prediction for the lithium ion battery packs

SoC estimation for lithium-ion batteries : review and future challenges

ABSTRACT: Energy storage emerged as a top concern for the modern cities, and the choice of the lithium-ion chemistry battery technology as an effective solution for storage applications proved to be a highly efficient option. State of charge (SoC) represents the available battery capacity and is one of the most important states that need to be monitored to optimize the performance and extend the lifetime of batteries. This review summarizes the methods for SoC estimation for lithium-ion batteries (LiBs). The SoC estimation methods are presented focusing on the description of the techniques and the elaboration of their weaknesses for the use in on-line battery management systems (BMS) applications. SoC estimation is a challenging task hindered by considerable changes in battery characteristics over its lifetime due to aging and to the distinct nonlinear behavior. This has led scholars to propose different methods that clearly raised the challenge of establishing a relationship between the accuracy and robustness of the methods, and their low complexity to be implemented. This paper publishes an exhaustive review of the works presented during the last five years, where the tendency of the estimation techniques has been oriented toward a mixture of probabilistic techniques and some artificial intelligence

Adaptive iterative working state prediction based on the double unscented transformation and dynamic functioning for unmanned aerial vehicle lithium-ion batteries.

In lithium-ion batteries, the accuracy of estimation of the state of charge is a core parameter which will determine the power control accuracy and management reliability of the energy storage systems. When using unscented Kalman filtering to estimate the charge of lithium-ion batteries, if the pulse current change rate is too high, the tracking effects of algorithms will not be optimal, with high estimation errors. In this study, the unscented Kalman filtering algorithm is improved to solve the above problems and boost the Kalman gain with dynamic function modules, so as to improve system stability. The closed-circuit voltage of the system is predicted with two non-linear transformations, so as to improve the accuracy of the system. Meanwhile, an adaptive algorithm is developed to predict and correct the system noises and observation noises, thus enhancing the robustness of the system. Experiments show that the maximum estimation error of the second-order Circuit Model is controlled to less than 0.20V. Under various simulation conditions and interference factors, the estimation error of the unscented Kalman filtering is as high as 2%, but that of the improved Kalman filtering algorithm are kept well under 1.00%, with the errors reduced by 0.80%, therefore laying a sound foundation for the follow-up research on the battery management system

A comprehensive working state monitoring method for power battery packs considering state of balance and aging correction.

A comprehensive working state monitoring method is proposed to protect the power lithium-ion battery packs, implying accurate estimation effect but using minimal time demand of self-learning treatment. A novel state of charge estimation model is conducted by using the improved unscented Kalman filtering method, in which the state of balance and aging process correction is considered, guaranteeing the powered battery supply reliability effectively. In order to realize the equilibrium state evaluation among the internal battery cells, the numerical description and evaluation is putting forward, in which the improved variation coefficient is introduced into the iterative calculation process. The intermittent measurement and real-time calibration calculation process is applied to characterize the capacity change of the battery pack towards the cycling maintenance number, according to which the aging process impact correction can be investigated. This approach is different to the traditional methods by considering the multi-input parameters with real-time correction, in which every calculation step is investigated to realize the working state estimation by using the synthesis algorithm. The state of charge estimation error is 1.83%, providing the technical support for the reliable power supply application of the lithium-ion battery packs