Physics-based prognostics of implantable-grade lithium-ion battery for remaining useful life prediction

Lui, Yu Hui; Li, Meng; Downey, Austin; Shen, Sheng; Nemani, Venkat Pavan; Ye, Hui; VanElzen, Collette M.; Jain, Geetika; Hu, Shan; Laflamme, Simon; Hu, Chao

doi:10.1016/j.jpowsour.2020.229327

Cited by 67 publications

(23 citation statements)

References 50 publications

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“…The indicators (interval points and integral of voltage vs. time as features; a time constant from the charging current curve; minimum and average temperature; IC-based and DV-based; cycle number etc.) from the charging curves can be explored because the charging curves are stable and the charging profile is usually fixed before charging is carried out [33][34][35][36][37][38]. The selected indicators from the charging curves shall be mapped to estimate the SoC, SoH and RUL using deep learning networks combined with transfer learning.…”

Section: Research Problemmentioning

confidence: 99%

Reconfigurable battery systems: Challenges and safety solutions using intelligent system framework based on digital twins

Garg

Mou

et al. 2022

IET Collab Intel Manufact

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Research on Reconfigurable Battery Systems (RBS) is gaining emphasis over the traditional fixed topology of the battery pack due to its advantages of adapting flexible topology (series‐parallel) during its operation in the pack for meeting the non‐linear time‐dependent load requirements. There could emerge serious issues such as those related to safety due to malfunction of the switching circuit, heat generation from switches during frequent switching of circuits, charging temperature rise, increased charging time, sensing issues arising from the use of low‐accuracy voltage/current sensors, state of charge/state of health estimation, and cost issues due to the use of increasing number of switches, fuses, contactors, relays, circuit breakers etc. To address these mentioned issues, the problem of optimal switching circuit topology for RBS is formulated as a mathematical multi‐objective optimisation problem. An intelligent system framework based on digital twins is proposed. The proposed framework is further extended to a life cycle management approach that includes the interactions among pack design, pack assembly and operational and recycling levels. This could provide greater access of real‐time big data cloud storage to the battery designers, manufacturers and recycling industries, who can make use of it to optimise their designs, systems and operations.

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Section: Research Problemmentioning

confidence: 99%

Reconfigurable battery systems: Challenges and safety solutions using intelligent system framework based on digital twins

Garg

Mou

et al. 2022

IET Collab Intel Manufact

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“…PoF methods have been tested on electronic components in monitoring the health of electronic components by Pecht et al [ 14 ]. The RUL of lithium-ion batteries has been predicted by physics-based models in [ 15 ]. There are also several publications that address prognostics modeling based on evolutionary methods derived from bio-inspired [ 16 , 17 ] and neuro-inspired algorithms [ 18 , 19 ].…”

Section: Review Of Key Concepts and Trendsmentioning

confidence: 99%

Smart Prognostics and Health Management (SPHM) in Smart Manufacturing: An Interoperable Framework

Sundaram

Zeid

2021

Sensors

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Advances in the manufacturing industry have led to modern approaches such as Industry 4.0, Cyber-Physical Systems, Smart Manufacturing (SM) and Digital Twins. The traditional manufacturing architecture that consisted of hierarchical layers has evolved into a hierarchy-free network in which all the areas of a manufacturing enterprise are interconnected. The field devices on the shop floor generate large amounts of data that can be useful for maintenance planning. Prognostics and Health Management (PHM) approaches use this data and help us in fault detection and Remaining Useful Life (RUL) estimation. Although there is a significant amount of research primarily focused on tool wear prediction and Condition-Based Monitoring (CBM), there is not much importance given to the multiple facets of PHM. This paper conducts a review of PHM approaches, the current research trends and proposes a three-phased interoperable framework to implement Smart Prognostics and Health Management (SPHM). The uniqueness of SPHM lies in its framework, which makes it applicable to any manufacturing operation across the industry. The framework consists of three phases: Phase 1 consists of the shopfloor setup and data acquisition steps, Phase 2 describes steps to prepare and analyze the data and Phase 3 consists of modeling, predictions and deployment. The first two phases of SPHM are addressed in detail and an overview is provided for the third phase, which is a part of ongoing research. As a use-case, the first two phases of the SPHM framework are applied to data from a milling machine operation.

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“…The more common equivalent circuit model are mainly the RC model, PNGV model, Thevenin model, Rint model, and so on. RUL prediction method based on the equivalent circuit model relative to the electrochemical model is more simple 23 . However, the equivalent circuit model cannot fully reflect the electrochemical reaction of lithium‐ion battery internal features, hence the dynamic characteristics of lithium‐ion batteries cannot be fully reflected 24 …”

Section: Introductionmentioning

confidence: 99%

“…RUL prediction method based on the equivalent circuit model relative to the electrochemical model is more simple. 23 However, the equivalent circuit model cannot fully reflect the electrochemical reaction of lithium-ion battery internal features, hence the dynamic characteristics of lithium-ion batteries cannot be fully reflected. 24 The RUL prediction method based on the empirical model does not need analysis of the internal electrochemistry reaction of the battery, 25 which is the most commonly used RUL prediction method in the laboratory stage.…”

Section: Introductionmentioning

confidence: 99%

A novel cuckoo search particle filtering strategy for the remaining useful life prediction of the lithium‐ion batteries in hybrid electric vehicle

Ren

Wang

Chen

et al. 2022

Intl J of Energy Research

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Summary The remaining useful life (RUL) is a core parameter of the battery management system. To realize accurately predict the RUL, the paper takes the National Aeronautics and Space Administration battery test data set as the research object, and a battery capacity degradation model based on an exponential growth model is built to characterize the battery aging process. A novel cuckoo search optimization particle filtering algorithm is proposed for the RUL prediction by transferring the particles in the prior distribution region of the particle filtering algorithm to the maximum likelihood region. The initial cycle numbers are set differently, the capacity decay process of the four groups of batteries can be predicted completely, and the RUL of the batteries can be obtained. Compared with the commonly used particle filtering and unscented particle filtering algorithms, the results show that the proposed method has obvious advantages in the relative error of prediction and resampling rate under the four datasets. For the B0005 dataset, the relative errors are 4.8%, 3.2%, and 0.8%, respectively under 30, 50, and 70 cycles, corresponding to the particle filtering algorithm errors are 10.3%, 7.9%, and 4.8%, and the unscented particle filter algorithm errors are 6.3%, 4.8%, and 2.4%, respectively. In addition, the method for RUL prediction present in the paper has a high confidence level, and low resampling rate, which plays an important role in promoting the further application of lithium‐ion batteries. Highlights A novel cuckoo search optimization particle filtering algorithm is proposed. Different battery aging cycle number is distinguished to predict. Multiple battery RUL prediction is realized. RUL prediction has high prediction accuracy and high confidence.

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Physics-based prognostics of implantable-grade lithium-ion battery for remaining useful life prediction

Cited by 67 publications

References 50 publications

Reconfigurable battery systems: Challenges and safety solutions using intelligent system framework based on digital twins

Reconfigurable battery systems: Challenges and safety solutions using intelligent system framework based on digital twins

Smart Prognostics and Health Management (SPHM) in Smart Manufacturing: An Interoperable Framework

A novel cuckoo search particle filtering strategy for the remaining useful life prediction of the lithium‐ion batteries in hybrid electric vehicle

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