Journal ArticleDOI
Aging prediction and state of charge estimation of a LiFePO 4 battery using input time-delayed neural networks
TLDR
In this paper, an intelligent state of charge (SOC) and state of health (SOH) estimation method for lithium-ion batteries using an input time-delayed neural network is presented.About:
This article is published in Electric Power Systems Research.The article was published on 2017-05-01. It has received 106 citations till now. The article focuses on the topics: State of charge & Battery (electricity).read more
Citations
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Journal ArticleDOI
State of Charge and State of Health Estimation for Lithium Batteries Using Recurrent Neural Networks
TL;DR: A nonlinear autoregressive with exogenous inputs (NARX) architecture of the DDRN is designed for both state of charge (SOC) and state of health (SOH) estimation.
Journal ArticleDOI
Recent progress of biomass-derived carbon materials for supercapacitors
TL;DR: In this article, the relationship between the species of biomass-based electrode and properties of supercapacitors is systematically discussed, and the influence of specific morphologies, heteroatom-introducing and graphitization degree of active carbon on the electrochemical properties are analyzed in detail.
Journal ArticleDOI
Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation
Muhammad Umair Ali,Amad Zafar,Sarvar Hussain Nengroo,Sadam Hussain,Muhammad Junaid Alvi,Hee-Je Kim +5 more
TL;DR: In this paper, Li-ion batteries have attracted considerable attention in the EV industry owing to their high energy density, lifespan, nominal voltage, power density, and cost, and a smart battery management system is one of the essential components; it not only measures the states of battery accurately, but also ensures safe operation and prolongs the battery life.
Journal ArticleDOI
SoC Estimation for Lithium-ion Batteries: Review and Future Challenges
TL;DR: A review of state of charge (SoC) estimation for lithium-ion batteries is presented in this article, focusing on the description of the techniques and the elaboration of their weaknesses for the use in online battery management systems (BMS) applications.
Journal ArticleDOI
Machine Learning Applied to Electrified Vehicle Battery State of Charge and State of Health Estimation: State-of-the-Art
TL;DR: A survey of battery state estimation methods based on ML approaches such as feedforward neural networks, recurrent neural networks (RNNs), support vector machines (SVM), radial basis functions (RBF), and Hamming networks is provided.
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.
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Battery, Ultracapacitor, Fuel Cell, and Hybrid Energy Storage Systems for Electric, Hybrid Electric, Fuel Cell, and Plug-In Hybrid Electric Vehicles: State of the Art
Alireza Khaligh,Zhihao Li +1 more
TL;DR: In this paper, the authors present state-of-the-art energy storage topologies for hybrid electric vehicles and plug-in hybrid vehicles (PHEVs) and compare battery, UC, and fuel cell technologies.
Journal ArticleDOI
State-of-Charge Estimation for Lithium-Ion Batteries Using Neural Networks and EKF
TL;DR: This paper presents a method for modeling and estimation of the state of charge (SOC) of lithium-ion (Li-Ion) batteries using neural networks (NNs) and the extended Kalman filter (EKF).
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State-of-Charge Estimation of the Lithium-Ion Battery Using an Adaptive Extended Kalman Filter Based on an Improved Thevenin Model
TL;DR: An adaptive Kalman filter algorithm that can greatly improve the dependence of the traditional filter algorithm on the battery model is employed and is evaluated by experiments with federal urban driving schedules, showing that the proposed SOC estimation using AEKF is more accurate and reliable than that using EKF.
Journal ArticleDOI
Nonlinear observers for predicting state-of-charge and state-of-health of lead-acid batteries for hybrid-electric vehicles
TL;DR: Approaches based on the well-known Kalman Filter and ExtendedKalman Filter are presented, using a generic cell model, to provide correction for offset, drift, and long-term state divergence-an unfortunate feature of more traditional coulomb-counting techniques.