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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Journal ArticleDOI
Effect analysis on SOC values of the power lithium manganate battery during discharging process and its intelligent estimation
TL;DR: In this paper , a coupled electrochemical-thermal model of the power lithium manganate battery under discharging process is established and verified, and its maximum surface temperature errors at the test point under 0.5C (C is discharging rate) and 1.0 C are 1.08 K and 0.95 K, respectively.
Journal ArticleDOI
An Online State of Charge Estimation Algorithm for Lithium-Ion Batteries Using an Improved Adaptive Cubature Kalman Filter
TL;DR: In this article, an improved adaptive Cubature Kalman filter (ACKF) was proposed to improve the accuracy and reliability of battery state of charge (SOC) estimation, and the battery model parameters were identified with the forgetting factor recursive least squares (FRLS) algorithm.
Journal ArticleDOI
Model-Based Lithium-Ion Battery Resistance Estimation From Electric Vehicle Operating Data
TL;DR: This paper suggests a method to estimate the 10-s discharge resistance, which is an established battery figure of merit from laboratory testing, without performing the laboratory test, and is suitable for onboard application.
Journal ArticleDOI
Lithium–Sulfur Battery State-of-Charge Observability Analysis and Estimation
TL;DR: A new framework is proposed consisting of online battery parameter identification in conjunction with an estimator that is trained to use the identified parameters to predict SOC, which demonstrates that the Li–S cell model is not fully observable because of the particular shape of cell's open-circuit voltage curve.
Journal ArticleDOI
State-of-Charge Observer Design for Batteries With Online Model Parameter Identification: A Robust Approach
Quan Ouyang,Jian Chen,Jian Zheng +2 more
TL;DR: A robust recursive-least-squares algorithm is utilized for the model parameters online extraction, which avoids unnecessary experiments prior to SOC estimation for parameter identification, and can effectively guarantee the parameter identification performance in spite of outliers in battery measurement signals.
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.