Machine<scp>learning‐based</scp>approach for useful capacity prediction of<scp>second‐life</scp>batteries employing appropriate input selection

Bhatt, Ankit; Ongsakul, Weerakorn; Madhu, Nimal; Singh, Jai Govind

doi:10.1002/er.7160

Cited by 15 publications

(11 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, these techniques' dependability is in question since they fail to account for the battery degradation curves' extreme nonlinearity. It can be seen from this review that researchers have now developed data-driven methods using machine learning algorithms including neural networks [9,38,79], advanced forecasting algorithms [8,9,53,55] and also, linear and non-linear regression techniques [54,79] for handling this non-linearity. The bi-staged non-linear regression technique [54] seems to give approximately accurate results but this study only discussed a battery with above 80% SOH, the results are not applicable to second-life battery.…”

Section: Discussionmentioning

confidence: 99%

“…In [55] a forecasting model to predict battery capacity estimation both for ageing and regeneration phenomenon is presented. However, this study does not use data from an actual electrical vehicle battery so in the future proposed model can be applied to real-life battery data from different compositions and manufacturers.…”

Section: Battery Soh Estimation and Ageing Modelsmentioning

confidence: 99%

See 1 more Smart Citation

A survey of second-life batteries based on techno-economic perspective and applications-based analysis

et al. 2023

View full text Add to dashboard Cite

The penetration of electrical vehicles (EVs) is exponentially rising to decarbonize the transport sector resulting in the research problem regarding the future of their retired batteries. Landfill disposal poses an environmental hazard, therefore, recycling or reusing them as second-life batteries (SLBs) are the inevitable options. Reusing the EV batteries with significant remaining useful life in stationary storage applications maximizes the economic benefits while extending the useful lifetime before recycling. Following a critical review of the research in SLBs, the key areas were identified as accurate State of Health (SOH) estimation, optimization of health indicators, battery life cycle assessment including repurposing, End-Of-Life (EOL) extension techniques and significance of first-life degradation data on ageing in second-life applications. The inconsistencies found in the reviewed literature showed that the absence of degradation data from first as well as second life, has a serious impact on accurate remaining useful life (RUL) prediction and SOH estimation. This review, for the first time, critically surveyed the recent studies in the field of identification, selection and control of application-based health indicators in relation to the accurate SOH estimation, offering future research directions in this emerging research area. In addition to the technical challenges, this paper also analyzed the economic perspective of SLBs, highlighting the impact of accuracy in second-life SOH estimation and RUL extension on their projected revenue in stationary storage applications. Lack of standard business model based on future market trends of energy and battery pricing and governing policies for SLBs are identified as urgent research gaps.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Battery Soh Estimation and Ageing Modelsmentioning

confidence: 99%

A survey of second-life batteries based on techno-economic perspective and applications-based analysis

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Thus, several works have been carried out to enable this important task. Bhatt et al [31] presented machine learning models based on MLP, LSTM, and CNN for predicting the second-life useful capacity of the discarded batteries that were proposed. The models were trained using the operational data from first life, from 100% to 80% SOH.…”

Section: Battery Performance Estimationmentioning

confidence: 99%

Second Life of Lithium-Ion Batteries of Electric Vehicles: A Short Review and Perspectives

et al. 2023

View full text Add to dashboard Cite

Technological advancement in storage systems has currently stimulated their use in miscellaneous applications. The devices have gained prominence due to their increased performance and efficiency, together with the recent global appeal for reducing the environmental impacts caused by generating power or by combustion vehicles. Many technologies have been developed to allow these devices to be reused or recycled. In this sense, the use of lithium-ion batteries, especially in electric vehicles, has been the central investigative theme. However, a drawback of this process is discarding used batteries. This work provides a short review of the techniques used for the second-life batteries of electric vehicles and presents the current positioning of the field, the steps involved in the process of reuse and a discussion on important references. In conclusion, some directions and perspectives of the field are shown.

show abstract

“…In order to repurpose SLBs for use in non-automotive applications, a proper visual inspection and technical verification of these batteries is necessary to ensure their reusability [15]. Battery ageing, or degradation, caused by repeated charging and discharging cycles, is an important phenomenon that affects the capacity fade and resistance growth of a battery [16].…”

Section: Introductionmentioning

confidence: 99%

“…The ability of a battery to store or deliver energy diminishes over time with ageing, making it crucial to accurately estimate (predict) its SoH or capacity by identifying/controlling the factors responsible for its ageing [17]. Such effective predictions can facilitate safe operation, reduce maintenance costs, extend the battery's life cycle, and identify replacement requirements where necessary [15]. Internal resistance and capacity are the most important factors used to estimate the SoH of a battery [18].…”

Section: Introductionmentioning

confidence: 99%

State of Health Prediction of Electric Vehicles’ Retired Batteries Based on First-Life Historical Degradation Data Using Predictive Time-Series Algorithms

Salek,

Resalati,

Azizi

et al. 2024

Mathematics

View full text Add to dashboard Cite

The exponential growth of electric and hybrid vehicles, now numbering close to 6 million on the roads, has highlighted the urgent need to address the environmental impact of their lithium-ion batteries as they approach their end-of-life stages. Repurposing these batteries as second-life batteries (SLBs) for less demanding non-automotive applications is a promising avenue for extending their usefulness and reducing environmental harm. However, the shorter lifespan of SLBs brings them perilously close to their ageing knee, a critical point where further use risks thermal runaway and safety hazards. To mitigate these risks, effective battery management systems must accurately predict the state of health of these batteries. In response to this challenge, this study employs time-series artificial intelligence (AI) models to forecast battery degradation parameters using historical data from their first life cycle. Through rigorous analysis of a lithium-ion NMC cylindrical cell, the study tracks the trends in capacity and internal resistance fade across both the initial and second life stages. Leveraging the insights gained from first-life data, predictive models such as the Holt–Winters method and the nonlinear autoregressive (NAR) neural network are trained to anticipate capacity and internal resistance values during the second life period. These models demonstrate high levels of accuracy, with a maximum error rate of only 2%. Notably, the NAR neural network-based algorithm stands out for its exceptional ability to predict local noise within internal resistance values. These findings hold significant implications for the development of specifically designed battery management systems tailored for second-life batteries.

show abstract

Machinelearning‐basedapproach for useful capacity prediction ofsecond‐lifebatteries employing appropriate input selection

Cited by 15 publications

References 59 publications

A survey of second-life batteries based on techno-economic perspective and applications-based analysis

A survey of second-life batteries based on techno-economic perspective and applications-based analysis

Second Life of Lithium-Ion Batteries of Electric Vehicles: A Short Review and Perspectives

State of Health Prediction of Electric Vehicles’ Retired Batteries Based on First-Life Historical Degradation Data Using Predictive Time-Series Algorithms

Contact Info

Product

Resources

About