Journal ArticleDOI
State of Charge Estimation of Lithium-Ion Batteries in Electric Drive Vehicles Using Extended Kalman Filtering
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TLDR
A more accurate battery state of charge (SOC) estimation method for electric drive vehicles is developed based on a nonlinear battery model and an extended Kalman filter supported by experimental data.Abstract:
In this paper, a more accurate battery state of charge (SOC) estimation method for electric drive vehicles is developed based on a nonlinear battery model and an extended Kalman filter (EKF) supported by experimental data. A nonlinear battery model is constructed by separating the model into a nonlinear open circuit voltage and a two-order resistance-capacitance model. EKF is used to eliminate the measurement and process noise and remove the need of prior knowledge of initial SOC. A hardware-in-the-loop test bench was built to validate the method. The experimental results show that the proposed method can estimate the battery SOC with high accuracy.read more
Citations
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Proceedings ArticleDOI
State of charge estimation of lead-carbon batteries in actual engineering
TL;DR: In this paper, the Improved Thevenin model was used as the battery mathematical model, and established the state-space equations, and then the best parameter estimation according with unconstrained nonlinear optimization methods.
Proceedings ArticleDOI
The State of Charge Estimation of Lithium-ion Batteries Using an Improved Extreme Learning Machine Approach
TL;DR: In this article , a particle swarm optimization-Extreme Learning Machine (PSO-ELM) algorithm was used to improve the estimation accuracy of state of charge (SOC) estimation for a lithium-ion battery.
Proceedings ArticleDOI
A discrete-time nonlinear observer for state of charge estimation of lithium-ion batteries
TL;DR: In this paper, a discrete-time nonlinear observer (DNLO) and a second-order resistor-capacitor (2RC) equivalent circuit model were proposed to estimate the state of charge (SOC) of the battery.
Book ChapterDOI
SOC Estimation of Extended Kalman Filter Based on the Model Data Optimization
Ziyi Fu,Xian Hua,Xiangwei Guo +2 more
TL;DR: This paper studies the SOC estimation using extended Kalman filter (EKF), which is based on the Thevenin equivalent circuit model, and a reasonable optimization method of the parameters of the model is presented to improve the SOC estimate accuracy.
References
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Journal ArticleDOI
Accurate electrical battery model capable of predicting runtime and I-V performance
Min Chen,Gabriel A. Rincon-Mora +1 more
TL;DR: An accurate, intuitive, and comprehensive electrical battery model is proposed and implemented in a Cadence environment that accounts for all dynamic characteristics of the battery, from nonlinear open-circuit voltage, current-, temperature-, cycle number-, and storage time-dependent capacity to transient response.
Journal ArticleDOI
Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 2. Modeling and identification
TL;DR: In this article, an extended Kalman filter (EKF) was used to estimate the battery state of charge, power fade, capacity fade, and instantaneous available power of a hybrid electric vehicle battery pack.
Journal ArticleDOI
Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 3. State and parameter estimation
TL;DR: In this article, extended Kalman filtering (EKF) is used to estimate battery state-of-charge, power fade, capacity fade, and instantaneous available power for hybrid-electric-vehicle battery packs.
Journal ArticleDOI
Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs Part 1. Background
TL;DR: In this paper, an extended Kalman filter (EKF) was proposed to estimate the battery state of charge, power fade, capacity fade, and instantaneous available power of a hybrid-electric-vehicle battery pack.
Journal ArticleDOI
Dynamic lithium-ion battery model for system simulation
TL;DR: In this article, the authors present a complete dynamic model of a lithium ion battery that is suitable for virtual prototyping of portable battery-powered systems, based on publicly available data such as the manufacturers' data sheets.