Battery-SOC Estimation for Hybrid-Power UAVs Using Fast-OCV Curve with Unscented Kalman Filters

Unmanned aerial vehicles (UAVs) have drawin increasing attention in recent years, and they are widely applied. Nevertheless, they are generally limited by poor flight endurance because of the limited energy density of their batteries. A robust power supply is indispensable for advanced UAVs; thus hybrid power might be a promising solution. State of charge (SOC) estimation is essential for the power systems of UAVs. The limitations of accurate SOC estimation can be partly ascribed to the inaccuracy of open circuit voltage (OCV), which is obtained through specific forms of identification. Considering the actual operation of a battery under hybrid conditions, this paper proposes a novel method, “fast OCV”, for obtaining the OCVs of batteries. It is proven that fast OCV offers great advantages, related to its simplicity, duration and cost, over traditional ways of obtaining OCV. Moreover, fast-OCV also shows better accuracy in SOC estimation than traditional OCV. Furthermore, this paper also proposes a new method, “batch mode”, for talking-data sampling for battery-parameter identification with the limited-memory recursive least-square algorithm. Compared with traditional the “single mode”, it presents good de-noising effect by making use of all the sampled battery’s terminal current and voltage data.This research was funded by Spanish Government, grant number PID2019-104793RB-C31 and PID2021-124335OB-C21; Comunidad de Madrid, grant number SEGVAUTO-4.0-CM (P2018/EMT-4362); and National Natural Science Foundation of China, grant number 52236007 and 52106176

The parameter update of Lithium-ion battery by the RSL algorithm for the SOC estimation under extended kalman filter (EKF-RLS)

The lithium-ion battery is the key power source of an electric vehicle. The cornerstone of safe transportation vehicles is reliable real-time state of charge (SOC) information. Since batteries are the primary form of energy storage in electric vehicles (EVs) and the smart grid, estimation of the state of charge is a critical need for batteries. The SOC estimate approach is considered to be precise and simple to apply for such applications. In this paper, After studying a battery model with an appropriate resistor-capacitor (RC) circuit, A lookup table derived from experimental studies describes the nonlinear connection between the Open Circuit Voltage Voc and the the state of charge. However, if temperature or SOC varies, the equivalent circuit model's characteristics will vary, decreasing the accuracy of SOC calculation. The recursive least squares (RLS) and nonlinear Extended Kalman filters are used in this research to offer a charge estimate technique with online parameter identification to handle this problem. RLS dynamically updates the Thevenin model's parameters. In order to improve the precision of SOC prediction under charge and discharge settings, we presented a regression least-squares-extended Kalman filter (RLS-EKF) estimation approach in this study. The objective of this research is to ensure the updating of the battery parameters and to evaluate the influence of this improvement on the convergence of the state of charge towards the real value. The simulation results suggest that the RLS EKF estimation technique, which is based on precise modeling, may greatly increase SOC estimation accuracy

The parameter update of Lithium-ion battery by the RSL algorithm for the SOC estimation under extended kalman filter (EKF-RLS)

The lithium-ion battery is the key power source of an electric vehicle. The cornerstone of safe transportation vehicles is reliable real-time state of charge (SOC) information. Since batteries are the primary form of energy storage in electric vehicles (EVs) and the smart grid, estimation of the state of charge is a critical need for batteries. The SOC estimate approach is considered to be precise and simple to apply for such applications. In this paper, After studying a battery model with an appropriate resistor-capacitor (RC) circuit, A lookup table derived from experimental studies describes the nonlinear connection between the Open Circuit Voltage Voc and the the state of charge. However, if temperature or SOC varies, the equivalent circuit model's characteristics will vary, decreasing the accuracy of SOC calculation. The recursive least squares (RLS) and nonlinear Extended Kalman filters are used in this research to offer a charge estimate technique with online parameter identification to handle this problem. RLS dynamically updates the Thevenin model's parameters. In order to improve the precision of SOC prediction under charge and discharge settings, we presented a regression least-squares-extended Kalman filter (RLS-EKF) estimation approach in this study. The objective of this research is to ensure the updating of the battery parameters and to evaluate the influence of this improvement on the convergence of the state of charge towards the real value. The simulation results suggest that the RLS EKF estimation technique, which is based on precise modeling, may greatly increase SOC estimation accuracy

A state-of-charge estimation method of the power lithium-ion battery in complex conditions based on adaptive square root extended Kalman filter.

The control strategy of electric vehicles mainly depends on the power battery state-of-charge estimation. One of the most important issues is the power lithium-ion battery state-of-charge (SOC) estimation. Compare with the extended Kalman filter algorithm, this paper proposed a novel adaptive square root extended Kalman filter together with the Thevenin equivalent circuit model which can solve the problem of filtering divergence caused by computer rounding errors. It uses Sage-Husa adaptive filter to update the noise variable, and performs square root decomposition on the covariance matrix to ensure its non-negative definiteness. Moreover, a multi-scale dual Kalman filter algorithm is used for joint estimation of SOC and capacity; the forgetting factor recursive least-square method is used for parameter identification. To verify the feasibility of the algorithm under complicated operating conditions, different types of dynamic working conditions are performed on the ternary lithium-ion battery. The proposed algorithm has robust and accurate SOC estimation results and can eliminate computer rounding errors to improve adaptability compared to the conventional extended Kalman filter algorithm

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

Scientometric research and critical analysis of battery state-of-charge estimation

With the advent of lithium-ion batteries (LIBs) and electric vehicle (EV) technology, the research on the battery State-of-Charge (SoC) estimation has begun to rise and develop rapidly. In order to objectively understand the current research status and development trends in the field of battery SoC estimation, this work uses an advanced search method to analyse the literature in the field of battery SoC estimation from 2004 to 2020 in the Web of Science (WoS) database. We employed bibliometrics analysis methods to make statistics on the publication year, the number of publications, discipline distribution, journal distribution, research institutions, application fields, test methods, analysis theories, and influencing factors in the field of battery SoC estimation. With using the Citespace software, a total of 2946 relevant research literature in the field of battery SoC estimation are analyzed. The research results show that the publication of relevant research documents keeps increasing from 2004 to 2020 in the field of battery SoC estimation. The research topics focus on battery model, management system, LIB, and EV. The research contents mainly involve Kalman filtering, wavelet neural network, impedance, and model predictive control. The main research approaches include model simulation, charging and discharging data recording, algorithm improvement, and environmental test. The research direction is shown to be more and more closely related to computer science and even artificial intelligence (AI). Intelligence, visualization, and multi-method collaboration are the future research trends of battery SoC estimation

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

Online modelling and state-of-charge estimation for lithium-titanate battery

Superior safety, is a promising energy storage element for electric vehicles. Its features can be fully utilised by using a fast charger and a high performance battery management system. Battery model is vital to a battery charger design for characterising the charging behaviours of a battery. Additionally, a robust state-ofcharge (SoC) estimation should be realised for a reliable battery management. This thesis develops a battery model for charger design and a robust method for SoC estimation by using MATLAB. The thesis proposed a transfer function-based battery model which is applicable for small-signal analysis and large-signal simulation of battery charger design, in order to capture the charging profiles of LTO battery. Busse’s adaptive rule, which has simple computations, is applied to improve the accuracy of Kalman filter-based SoC estimation. Busse’s adaptive Kalman filters are also applied for SoC estimation with online battery modelling to eliminate the complicated process of battery modelling. This study was conducted by using 2.4 V, 15 Ah LTO batteries. The batteries were tested with continuous current test and pulsed current test at several ambient temperatures (-25 ºC, 0 ºC, 25 ºC and 50 ºC) and charge/discharge currents (0.5 C, 1 C, 2 C). Additionally, modified dynamic stress tests at several temperatures (-15 ºC, 0 ºC, 15 ºC, 25 ºC, 35 ºC and 50 ºC) were also performed to test the battery under real EV environment. Results of the battery modelling showed that the developed transfer function-based battery model is accurate where the root-mean-square modelling error is less than 30 mV. The results also revealed that the Busse’s adaptive rule has effectively improved the Kalman filter-based SoC estimation for the case of offline battery model by giving a higher accuracy and shorter convergence time. Additionally, Busse’s adaptive Extended Kalman Filter gave a better accuracy in SoC estimation with online battery modelling. The proposed transfer function-based battery model provides a helpful solution for the battery charger design while the proposed Busse’s adaptive Kalman filter offers an accurate and robust SoC estimation for both offline and online battery models