Thermal Runaway Detection Method for Smart Electric Bicycle Charger by Jing Ning, Bing Xiao, Wenbin Zhao

“…ABSTRACT A novel algorithm for thermal runaway detection embedded in the electric bicycle charging system is proposed. …”
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Protection and Security Method for Multiple Energy Power Plant-Based Microgrids Using Dual Filtering Algorithm by Danni Liu, Shengda Wang, Weijia Su, Xiaojuan Zhang, Shichun Hui

“…Conventional fault detection methods often fail to address the unique dynamics of these MEPPBM, leading to delays in fault detection and classification. …”
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Operation Condition and Performance Test Analysis of Distributed Energy Storage Battery by Xuhao DU, Bingyu LI, Junjie MIAO, Xiaofan GUO

“…This paper deeply studied the state of charge (SOC) of energy storage battery, and proposed an estimation algorithm based on (extended kalman particle filter，EKPF) as the detection method. …”
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Novel intelligent MPP tracker and sliding mode control for decentralized street lighting systems using photovoltaic energy by Hussain Attia, Ali Al-Ataby, Maen Takruri, Amjad Omar

“…Unlike traditional decentralised street lighting solutions, which focus mainly on LED dimming based on motion detection or basic MPPT algorithms, this paper proposes a hybrid approach that integrates an intelligent ANN-based MPPT battery charger with a robust SMC for load driving. …”
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Lithium-ion battery state of health estimation using intelligent methods by Hemavathi S

“…In electric vehicle applications, detecting Li-ion battery degradation is essential to ensure safety and reliability. …”
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Online Spectroscopic Study on the Positive and the Negative Electrolytes in Vanadium Redox Flow Batteries by Le Liu, Jingyu Xi, Zenghua Wu, Wenguang Zhang, Haipeng Zhou, Weibin Li, Yonghong He

“…Traditional spectroscopic analysis based on the Beer-Lambert law cannot analyze the analyte with high concentration and interference between different compositions, such as the electrolyte in vanadium redox flow batteries (VRBs). Here we propose a new method for online detection of such analytes. …”
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Lithium Battery Thermal Runaway Warning Method Based on Multi-Feature Fusion by Mingwei DAI, Chunfu ZHANG, Jiawu YANG

“…[Conclusion] The early warning algorithm is able to accurately identify lithium batteries with abnormal temperature rise rates, and can promptly and precisely detect the timing and location of the opening of the safety valve in the lithium battery. …”
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Recognition of State of Health Based on Discharge Curve of Battery by Signal Temporal Logic by Jing Ning, Bing Xiao, Wenhui Zhong

“…As a means of quickly detecting whether the battery is in a healthy state, the accuracy difference is negligible, so the STL algorithm is apparently superior in terms of performance and realizability.…”
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Sensors Innovations for Smart Lithium-Based Batteries: Advancements, Opportunities, and Potential Challenges by Jamile Mohammadi Moradian, Amjad Ali, Xuehua Yan, Gang Pei, Shu Zhang, Ahmad Naveed, Khurram Shehzad, Zohreh Shahnavaz, Farooq Ahmad, Balal Yousaf

“…Highlights Sensors for smart Lithium-based batteries (LiBs) are classified based on their application into safety monitoring (i.e., temperature, pressure, and strain) to detect hazardous conditions and performance optimization (i.e., optical and electrochemical sensors) for monitoring factors such as state of charge and state of health. …”
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Battery Management System for Solar Power Plants in Uganda: An IoT-Driven Approach by Ssembalirwa, Denis, Cartland, Richard, Bature, U. I., Kitone, Isaac

“…Intelligent algorithms autonomously regulate charging and discharging cycles to prevent overcharging and deep discharge, optimizing battery performance. …”
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Microcontroller-Based Platform for Lithium-Ion Battery Charging and Experimental Evaluation of Charging Strategies by Laurentiu Marius Baicu, Mihaela Andrei, Bogdan Dumitrascu

“…Its open-source architecture and modular design make it highly suitable for research, educational use, and experimental development in battery management systems. Future enhancements may include the integration of adaptive algorithms based on internal resistance and temperature, enabling smarter and more efficient charging.…”
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An Energy-Efficient Battery Monitoring and Logging System for Agricultural Robotics with CAN Bus Integration by Soosaar Guido, Lillerand Tormi

“…A key innovation is its adaptive data acquisition algorithm, which adjusts polling frequency based on battery activity and temperature thresholds, significantly reducing power consumption without compromising responsiveness. …”
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Fault Diagnosis of Lithium-Ion Batteries Based on the Historical Trajectory of Remaining Discharge Capacity by Jiuchun Jiang, Bingrui Qu, Shuaibang Liu, Huan Yan, Zhen Zhang, Chun Chang

“…The method first utilizes the sparrow search algorithm (SSA) to identify the parameters of the second-order equivalent circuit model of the lithium-ion battery, and then estimates the state of charge (SOC) of the lithium-ion battery using the extended Kalman filter (EKF). …”
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Invisible Manipulation: Deep Reinforcement Learning-Enhanced Stealthy Attacks on Battery Energy Management Systems by Qi Xiao, Lidong Song, Jong Ha Woo, Rongxing Hu, Bei Xu, Kai Ye, Ning Lu

“…This unique DRL training and testing setup not only showcases the effectiveness of the Timed-SFDIA algorithm in evading detection and achieving diverse attack objectives but also underscores the critical role of high-fidelity, digital-twin based real-time simulation testbeds. …”
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Synthetic Data Generation for AI-Informed End-of-Line Testing for Lithium-Ion Battery Production by Tessa Krause, Daniel Nusko, Johannes Rittmann, Luciana Pitta Bauermann, Moritz Kroll, Carlo Holly

“…In this paper, we demonstrate a first-order physical modelling approach for generating synthetic data to pre-train artificial intelligence algorithms that perform anomaly detection on lithium-ion battery cells at the end-of-line. …”
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Exploiting Artificial Neural Networks for the State of Charge Estimation in EV/HV Battery Systems: A Review by Pierpaolo Dini, Davide Paolini

“…Specifically, ANN models excel at detecting subtle, complex patterns that reflect battery health and performance, crucial for accurate SOC estimation. …”
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Exploring Machine Learning and Deep Learning Approaches for Battery Management Systems in EVs: A Comprehensive Review by Sathish J., Ramash Kumar K., Saraswathi D.

“…Machine learning and deep learning algorithms mimic humans by focusing on statistical data and algorithms on a real-time basis. …”
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Real-time temperature prediction of large-scale lithium battery module driven by data based on few measurement points by Jiajie HAN, Qingyang YUAN, Yu LI, Bo ZHANG, Ke XUE, Tian LAN

“…This study presents a data-driven approach using the gappy proper orthogonal decomposition (Gappy POD) algorithm, a reduced-order modeling technique, for real-time temperature monitoring of large-scale battery modules. …”
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Capacity Estimation and Knee Point Prediction Using Electrochemical Impedance Spectroscopy for Lithium Metal Battery Degradation via Machine Learning by Qianli Si, Shoichi Matsuda, Yasunobu Ando, Toshiyuki Momma, Yoshitaka Tateyama

“…In this study, a machine learning (ML) framework is proposed that combines electrochemical impedance spectroscopy (EIS) with the XGBoost algorithm to develop two predictive models: one for estimating capacity degradation and another for detecting the knee point (KP)—a critical inflection point in the degradation trajectory. …”
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Dataset of noise signals generated by smart attackers for disrupting state of health and state of charge estimations of battery energy storage systemsMendeley by Alaa Selim, Huadong Mo, Hemanshu Pota

“…Additionally, we introduce a verification case using a different battery model and estimation algorithm to enhance generalization. …”
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