State of Health Estimation of Lithium Batteries for Automotive Applications with Artificial Neural Networks

Bonfitto, Angelo; Ezemobi, Ethelbert; Amati, Nicola; Feraco, Stefano; Tonoli, Andrea; Hegde, Shailesh

doi:10.23919/eeta.2019.8804567

Cited by 27 publications

(25 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similarly for studies of LIBs, a number of data-driven estimation models for Figure 1), comprising a pair of resistors R e , R ct , accounting for the resistance of the electrolyte and current collector foils, capacitor C dl for charge transfer effects and electrical double-layers, and a Warburg impedance Z W element representing diffusion. 35 the battery SoC, 15,39,40 SoH 12,[15][16][17][18]41 and RUL have been developed. 12,42,43 Many of these studies focus on online estimation of SoH by capacity, with the prevailing approach involving following the evolution of the current, terminal voltage and partial capacity curves of the battery with time as applied by previous works.…”

Section: Machine Learning For Soh Estimationmentioning

confidence: 99%

“…The high prediction accuracy is achieved based on application of highly complex NN models, with the caveat that as the number of neurons used to model the system increases, there is a larger requirement for training data to prevent over-fitting of the network to the training dataset, while the model becomes more computationally demanding due to the increased number of model parameters. 17 Most critically, it has been noted 6 that a principal limitation of many of these NN studies is the lack of detailed information regarding the battery ageing state, such as that exposed by the extraction of ECM parameters from EIS. Few approaches have been explored with an NN-based scheme to estimate cell SoH directly using ECM parameters, with prior studies proposing an extreme learning machine (ELM) monitoring the evolution of ECM parameters as SoH predictors with cell cycling 15 or a single hidden layer feed-forward NN 16 based on ECM parameters extracted with hybrid pulse-power characterisation (HPPC).…”

Section: Machine Learning For Soh Estimationmentioning

confidence: 99%

“…Recent years have seen exploration of numerous machine learning schemes to estimate SoH. 12,[15][16][17][18] Neural networks (NNs) in particular are an attractive solution to SoH estimation in that they can model complex, non-linear systems with cross-interactions between system variables without a detailed underlying theoretical framework of the system being considered. 12 This is useful for LIBs where the precise internal composition and design is often commercially sensitive and hence not accessible.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A rapid neural network–based state of health estimation scheme for screening of end of life electric vehicle batteries

Rastegarpanah

Hathaway

Ahmeid

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part I:

View full text Add to dashboard Cite

There is growing interest in recycling and re-use of electric vehicle batteries owing to their growing market share and use of high-value materials such as cobalt and nickel. To inform the subsequent applications at battery end of life, it is necessary to quantify their state of health. This study proposes an estimation scheme for the state of health of high-power lithium-ion batteries based on extraction of parameters from impedance data of 13 Nissan Leaf 2011 battery modules modelled by a modified Randles equivalent circuit model. Using the extracted parameters as predictors for the state of health, a baseline single hidden layer neural network was evaluated by root mean square and peak state of health prediction errors and refined using a Gaussian process optimisation procedure. The optimised neural network predicted state of health with a root mean square error of (1.729 ± 0.147)%, which is shown to be competitive with some of the most performant existing neural network–based state of health estimation schemes, and is expected to outperform the baseline model with ∼50 training samples. The use of equivalent circuit model parameters enables more in-depth analysis of the battery degradation state than many similar neural network–based schemes while maintaining similar accuracy despite a reduced dataset, while there is demonstrated potential for measurement times to be reduced to as little as 30 s with frequency targeting of the impedance measurements.

show abstract

Section: Machine Learning For Soh Estimationmentioning

confidence: 99%

Section: Machine Learning For Soh Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A rapid neural network–based state of health estimation scheme for screening of end of life electric vehicle batteries

Rastegarpanah

Hathaway

Ahmeid

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part I:

View full text Add to dashboard Cite

show abstract

“…The cooling system plays a fundamental role in the performance and the life of a battery [30] as well as in the performance of the electrical and electronics components [31], [32] which in turn have an influence on the battery life [33]. Furthermore, accurate systems for the estimation of the SOC and SOH could prolong the life of Lithium-ion batteries by avoiding deep discharge and charge cycles at extremely high or cold temperatures, which are one of the major factors that shorten the life of a battery [4], [5]. The efficiency of the battery depends on the collection rate and recycling efficiency.…”

Section: B) Technical Aspectsmentioning

confidence: 99%

“…Consequently, the public interest in Lithium-ion batteries is growing steadily worldwide [3]. In this framework, electric vehicles featuring one or more battery are one of the key technologies for the next generation of road transportation, along with novel algorithms for the battery State of Charge (SOC) and State of Health (SOH) monitoring and estimation [4], [5]. However, a wide range of raw materials and industrial processes is required for the manufacturing of Lithiumion batteries, resulting in supply risks, and a high economic importance of the production chain [6].…”

Section: Introductionmentioning

confidence: 99%

Economic, Technical and Environmental Aspects of Recycling Lithium Batteries: A Literature Review

Filomeno¹,

Feraco²

2020

GJRE

Self Cite

View full text Add to dashboard Cite

In the last few years, the automotive industry has been moving towards fuel-free and economically sustainable alternatives, motivated by the latest trends in the market and new regulations about CO2 emissions. Hybrid and electric vehicles feature a transmission drive with one or more electrical motors powered by Lithium batteries. Thus, Lithium batteries are increasingly used in onboard energy storage systems, leading new economical, technical and environmental challenges which are of fundamental importance in this early stage for the next automotive generation. Recycling materials from used Lithium batteries can also moderate the price of virgin materials, by reducing the price disposal as well as the dependence of manufacturers on exporting countries. Furthermore, recycling Lithium-ion batteries has significant environmental benefits, such as containing the risk of chemical pollution and improving safety in storage facilities for exhausted batteries worldwide. This paper aims to provide a comprehensive insight on Lithium-ion battery recycling for scientific research and industrial applications, examining the economic, technical and environmental aspects of this topic.

show abstract

A review of battery state of charge estimation and management systems: Models and future prospective

Hussein,

Aghmadi,

Abdelrahman

et al. 2024

WIREs Energy & Environment

View full text Add to dashboard Cite

Batteries are considered critical elements in most applications nowadays due to their power and energy density features. However, uncontrolled charging and discharging will negatively affect their functions and might result in a catastrophic failure of their applications. Hence, a battery management system (BMS) is mandated for their proper operation. One of the critical elements of any BMS is the state of charge (SoC) estimation process, which highly determines the needed action to maintain the battery's health and efficiency. Several methods were used to estimate the Lithium‐ion batteries (LIBs) SoC, depending on the LIBs model or any other suitable technique. This article provides a critical review of the existing SoC estimation approaches and the main LIB models with their pros and cons, their possibility to integrate with each other's for precise estimation results, and the applicability of these techniques in electric vehicles and utility applications with the commonly used standards and codes in these sectors. Moreover, this study will also explore a future framework for integrating digital twins (DTs) with BMSs for improved and advanced management. Based on this comprehensive review, it can be concluded that merging the model‐based estimation techniques with the data‐driven approaches with their promising development to determine the dynamic patterns inside the battery can efficiently achieve precise estimation results while reducing the complexity of these models. This integration will align also with the integration of digital twin technology to provide complete and accurate supervision for the BMSs.This article is categorized under: Emerging Technologies > Energy Storage Emerging Technologies > Digitalization Energy and Power Systems > Distributed Generation

show abstract

State of Health Estimation of Lithium Batteries for Automotive Applications with Artificial Neural Networks

Cited by 27 publications

References 16 publications

A rapid neural network–based state of health estimation scheme for screening of end of life electric vehicle batteries

A rapid neural network–based state of health estimation scheme for screening of end of life electric vehicle batteries

Economic, Technical and Environmental Aspects of Recycling Lithium Batteries: A Literature Review

A review of battery state of charge estimation and management systems: Models and future prospective

Contact Info

Product

Resources

About