Journal ArticleDOI
The Co-estimation of State of Charge, State of Health, and State of Function for Lithium-Ion Batteries in Electric Vehicles
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TLDR
This paper proposes a co-estimation scheme of state of charge, state of health (SOH), and state of function (SOF) for lithium-ion batteries in electric vehicles that is validated in a real battery management system with good real-time performance and convincible estimation accuracy.Abstract:
This paper proposes a co-estimation scheme of state of charge (SOC), state of health (SOH), and state of function (SOF) for lithium-ion batteries in electric vehicles. The co-estimation denotes that the SOC, SOH, and SOF are estimated simultaneously in real-time application. The model-based SOC estimation is fulfilled by the extended Kalman filter. The battery parameters related with the battery SOH and SOF are online identified using the recursive least square algorithm with a forgetting factor. The capacity and the maximum available output power are then estimated based on the identified parameters. The online update of the capacity and correlated parameters help improve the accuracy of the state estimation but with limited increase in the computation load, by making good use of the correlations among the states. The co-estimation scheme is validated in a real battery management system with good real-time performance and convincible estimation accuracy.read more
Citations
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Journal ArticleDOI
State estimation for advanced battery management: Key challenges and future trends
TL;DR: This paper presents a concise, understandable overview of existing methods, key issues, technical challenges, and future trends of the battery state estimation domain, for the first time, in SOC/SOE/SOH/SOP/SOT/SOS estimation.
Journal ArticleDOI
Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries
TL;DR: Challenge steps in the implementation of KF family algorithms in model-based online SOC estimation processes, such as selection of battery model, initial SOC and filter tuning, are elaborated for the efficient development of a battery management system, especially for EV application.
Journal ArticleDOI
A review of the state of health for lithium-ion batteries: Research status and suggestions
TL;DR: A discussion on the aging reasons for LIBs is discussed, the SOH prediction method based on the classification framework is introduced, and the key benefits and drawbacks of each method are analyzed.
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
Online State-of-Health Estimation for Li-Ion Battery Using Partial Charging Segment Based on Support Vector Machine
TL;DR: Train, validation, and test are conducted for two commercial Li-ion batteries with Li(NiCoMn)1/3O2 cathode and graphite anode, indicating that the algorithm can estimate the battery SOH with less than 2% error for 80% of all the cases, and less than 3%error for 95% ofall the cases.
References
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Journal ArticleDOI
A review on the key issues for lithium-ion battery management in electric vehicles
TL;DR: In this article, a brief introduction to the composition of the battery management system (BMS) and its key issues such as battery cell voltage measurement, battery states estimation, battery uniformity and equalization, battery fault diagnosis and so on, is given.
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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.
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A comparative study of equivalent circuit models for Li-ion batteries
TL;DR: In this paper, a comparative study of twelve equivalent circuit models for Li-ion batteries is presented, which are selected from state-of-the-art lumped models reported in the literature.
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Combined State of Charge and State of Health estimation over lithium-ion battery cell cycle lifespan for electric vehicles
TL;DR: In this paper, a combined state of charge (SOC) and SOH (State Of Health) estimation method over the lifespan of a lithium-ion battery is proposed, where the SOH is estimated in real-time and the capacity and internal ohmic resistance are updated offline.
Journal ArticleDOI
A holistic aging model for Li(NiMnCo)O2 based 18650 lithium-ion batteries
TL;DR: In this paper, a holistic aging model from accelerated aging tests is presented to analyze the impact of different impact factors on lithium-ion battery aging and lifetime estimation, which is a fundamental aspect for successful market introduction in high-priced goods like electric mobility.