GPR-Bi-LSTM power battery health state estimation and remaining life prediction based on ICEEMDAN algorithm

Li, Huipeng; Yao, Yiming; Hou, Jiayu; Zhou, Zhe; Cai, Zhiduan; Li, Zuxin

doi:10.1109/iscsic57216.2022.00041

Cited by 3 publications

(1 citation statement)

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“…A data-driven approach for SoH prediction can be introduced using GPR, selected for its ability to model complex data relationships and capture prediction uncertainty without relying on future load information or DL-based prognostics [82,83]. In [84], the results on the NASA battery dataset show that the proposed method does not exceed 1% error in early SoH predictions.…”

Section: Battery Health Prediction Dynamic Through Aimentioning

confidence: 99%

Exploring the Synergy of Artificial Intelligence in Energy Storage Systems for Electric Vehicles

Miraftabzadeh,

Longo,

Di Martino

et al. 2024

Electronics

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The integration of Artificial Intelligence (AI) in Energy Storage Systems (ESS) for Electric Vehicles (EVs) has emerged as a pivotal solution to address the challenges of energy efficiency, battery degradation, and optimal power management. The capability of such systems to differ from theoretical modeling enhances their applicability across various domains. The vast amount of data available today has enabled AI to be trained and to predict the behavior of complex systems with a high degree of accuracy. As we move towards a more sustainable future, the electrification of vehicles and integrating electric systems for energy storage are becoming increasingly important and need to be addressed. The synergy of AI and ESS enhances the overall efficiency of electric vehicles and plays a crucial role in shaping a sustainable and intelligent energy ecosystem. To the best of the authors’ knowledge, AI applications in energy storage systems for the integration of electric vehicles have not been explicitly reviewed. The research investigates the importance of AI advancements in energy storage systems for electric vehicles, specifically focusing on Battery Management Systems (BMS), Power Quality (PQ) issues, predicting battery State-of-Charge (SOC) and State-of-Health (SOH), and exploring the potential for integrating Renewable Energy Sources with EV charging needs and optimizing charging cycles. This study examined all topics to identify the most commonly used methods, which were analyzed based on their characteristics and potential. Future trends were identified by exploring emerging techniques introduced in recent literature contributions published since 2017.

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Section: Battery Health Prediction Dynamic Through Aimentioning

confidence: 99%