A State of Charge Estimator Based Extended Kalman Filter Using an Electrochemistry-Based Equivalent Circuit Model for Lithium-Ion Batteries
TLDR
In this paper, an improved equivalent circuit model (ECM) considering partial electrochemical properties is developed for accurate state-of-charge (SOC) estimation, where the solid phase diffusion process is calculated by a simple equation about particle surface SOC, and the double layer is simulated by two resistance-capacitance (RC) networks.Abstract:
In this paper, an improved equivalent circuit model (ECM) considering partial electrochemical properties is developed for accurate state-of-charge (SOC). In the proposed model, the solid-phase diffusion process is calculated by a simple equation about particle surface SOC, and the double layer is simulated by two resistance-capacitance (RC) networks. To improve the global accuracy of the model, a subarea parameter-identification method based on particle swarm optimization is proposed, in order to determine the optimal model parameters in the entire SOC area. Then, an SOC estimator is developed based on extended kalman filter. The comparative study shows that a model considering solid-phase diffusion with two RC networks is the best choice. Finally, experimental results show that the accuracy of the proposed model is one times higher than that of the traditional ECM in the low SOC area, and is able to estimate SOC with errors less than 1% in the entire SOC area. Furthermore, estimation results of two types of batteries under two working conditions indicate that the developed model and SOC estimator have satisfactory global accuracy and guaranteed robustness with low computational complexity, which can be applied in real-time situations.read more
Citations
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State of charge estimation of a Li-ion battery based on extended Kalman filtering and sensor bias
Journal ArticleDOI
Online detection of early stage internal short circuits in series-connected lithium-ion battery packs based on state-of-charge correlation
Xin Lai,Xin Lai,Yi Wei,Xiangdong Kong,Xuebing Han,Long Zhou,Tao Sun,Yuejiu Zheng,Yuejiu Zheng +8 more
TL;DR: The results show that the proposed method is fast, highly accurate, and that it enables the online detection of an early stage ISC of 100 Ω under dynamic conditions within 20.4 h, which is suitable for improving battery safety.
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Critical Review of Intelligent Battery Systems: Challenges, Implementation, and Potential for Electric Vehicles
Lidiya Komsiyska,Tobias Buchberger,Simon Diehl,Moritz Ehrensberger,Christian Hanzl,Christoph Hartmann,Markus Hölzle,Jan Kleiner,Meinert Lewerenz,Bernhard Liebhart,Michael Schmid,Dominik Schneider,Sascha Speer,Julia Stöttner,Christoph Terbrack,Michael Hinterberger,Christian Endisch +16 more
TL;DR: This review provides an overview of new strategies to address the current challenges of automotive battery systems: Intelligent Battery Systems and touches on sensing, battery topologies and management, switching elements, communication architecture, and impact on the single-cell.
Journal ArticleDOI
Design of an Effective State of Charge Estimation Method for a Lithium-Ion Battery Pack Using Extended Kalman Filter and Artificial Neural Network
Van Quan Dao,Minh-Chau Dinh,Chang Soon Kim,Minwon Park,Chil-Hoon Doh,Jeong Hyo Bae,Myung-Kwan Lee,Jianyong Liu,Zhiguo Bai +8 more
TL;DR: This paper presented the design of an effective SOC estimation method for a LiB pack Battery Management System (BMS) based on Kalman Filter (KF) and Artificial Neural Network (ANN) and proposed a combined mode EKF-ANN that integrates the estimation of the EKf into the ANN.
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A Novel Screening Method Based on a Partially Discharging Curve Using a Genetic Algorithm and Back-Propagation Model for the Cascade Utilization of Retired Lithium-Ion Batteries
TL;DR: In this article, a novel screening method based on partial discharge curves using a genetic algorithm and back-propagation (GA-BP) neural network for the retired cells is proposed, and the results showed that the proposed method is feasible and the maximum error of capacity estimation was 2.951%.
References
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A comparative study of different equivalent circuit models for estimating state-of-charge of lithium-ion batteries
Xin Lai,Yuejiu Zheng,Tao Sun +2 more
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