Offline multiobjective optimization for fast charging and reduced degradation in lithium ion battery cells

Lam, Fredric; Allam, Anirudh; Joe, Won Tae; Choi, Yeonjoo; Onori, Simona

doi:10.23919/acc50511.2021.9483051

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…This enables researchers to accurately estimate the state of health (SOH) and predict the remaining useful life (RUL) of batteries [14]. Additionally, the ECM has been enhanced with thermal models to account for phenomena such as solid electrolyte interface (SEI) layer growth-a key indicator of battery degradation [15]-furthering the precision of lifetime prediction methods [16]. The pseudo-two-dimensional (P2D) model [17], a prominent ECM variant, applies porous electrode theory principles, focusing on the order reduction and simplification of kinetic equations [18], and also serves as a competitive model for SOH estimations [19].…”

Section: Introductionmentioning

confidence: 99%

Attention Mechanism-Based Neural Network for Prediction of Battery Cycle Life in the Presence of Missing Data

Wang,

Jiang

2024

Batteries

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As batteries become widespread applications across various domains, the prediction of battery cycle life has attracted increasing attention. However, the intricate internal mechanisms of batteries pose challenges to achieving accurate battery lifetime prediction, and the inherent patterns within temporal data from battery experiments are often elusive. Meanwhile, the commonality of missing data in real-world battery usage further complicates accurate lifetime prediction. To address these issues, this article develops a self-attention-based neural network (NN) to precisely forecast battery cycle life, leveraging an attention mechanism that proficiently manages time-series data without the need for recurrent frameworks and adeptly handles the data-missing scenarios. Furthermore, a two-stage training approach is adopted, where certain network hyperparameters are fine-tuned in a sequential manner to enhance training efficacy. The results show that the proposed self-attention-based NN approach not only achieves superior predictive precision compared with the benchmarks including Elastic Net and CNN-LSTM but also maintains resilience against missing-data scenarios, ensuring reliable battery lifetime predictions. This work highlights the superior performance of utilizing attention mechanism for battery cycle life prognostics.

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Section: Introductionmentioning

confidence: 99%

Attention Mechanism-Based Neural Network for Prediction of Battery Cycle Life in the Presence of Missing Data

Wang,

Jiang

2024

Batteries

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“…Electrochemical models have been used to improve charging algorithms with methods such as the Legendre-Gauss-Radau method [18], MPC [21], [22], open loop protocols [23], [24], Pontryagin's minimum principle [25], optimization of cell life alone [26], and single-timestep optimization [27]. However, electrochemical model-based BMSs have yet to gain wide adoption because of their extensive computational time.…”

Section: Introductionmentioning

confidence: 99%

A Model Predictive Control Scheme for Fast Charging via Accurate Quadratic Battery Models

Bills

Salazar

Zhang

et al. 2022

2022 American Control Conference (ACC)

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“…The degradation modes as identified via this method have uses beyond the protocol we developed herein and applications beyond electric aviation. One use-case for electrochemical modeling is the development of model-based fast charging protocols. − These protocols aim to reduce charging time while constraining internal quantities related to safety and degradation . They assume that the model parameters are known.…”

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confidence: 99%

Massively Distributed Bayesian Analysis of Electric Aircraft Battery Degradation

Bills,

Fredericks,

Sulzer

et al. 2023

ACS Energy Lett.

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Electric vertical takeoff and landing (EVTOL) aircraft have high power and energy requirements that must be understood throughout their batteries’ life. Using a fast electrochemical model and high throughput computing, we independently and simultaneously sample the degradation modes for 21392 cycles of an EVTOL aircraft battery data set, obtaining a posterior probability distribution for each degradation mode throughout the life of the aircraft. The model shows a median error of 32.5 mV (or 1.9%) across all cycles. We conduct an identifiability analysis on the generated mode distributions. We analyze correlations between the modes and cycling characteristics, finding that depth of discharge, temperature, and charging current all have significant impacts on degradation. We introduce a protocol for early identification of active degradation mechanisms, identifying electrolyte oxidation, active material dissolution, and growth of a solid–electrolyte interphase as the most likely causes of battery degradation. Finally, we discuss other applications of large-scale sampling of battery degradation modes.

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Offline multiobjective optimization for fast charging and reduced degradation in lithium ion battery cells

Cited by 3 publications

References 17 publications

Attention Mechanism-Based Neural Network for Prediction of Battery Cycle Life in the Presence of Missing Data

Attention Mechanism-Based Neural Network for Prediction of Battery Cycle Life in the Presence of Missing Data

A Model Predictive Control Scheme for Fast Charging via Accurate Quadratic Battery Models

Massively Distributed Bayesian Analysis of Electric Aircraft Battery Degradation

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