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1. Battery health management using physics-informed machine learning: Online degradation modeling and remaining useful life prediction

   https://doi.org/10.1016/j.ymssp.2022.109347  

   Shi, Junchuan; Rivera, Alexis; Wu, Dazhong. (November 2022, Mechanical systems and signal processing)

   Wang, Dong (Ed.)

   Lithium-ion batteries have been extensively used to power portable electronics, electric vehicles, and unmanned aerial vehicles over the past decade. Aging decreases the capacity of Lithium-ion batteries. Therefore, accurate remaining useful life (RUL) prediction is critical to the reliability, safety, and efficiency of the Lithium-ion battery-powered systems. However, battery aging is a complex electrochemical process affected by internal aging mechanisms and operating conditions (e.g., cycle time, environmental temperature, and loading condition). In this paper, a physics-informed machine learning method is proposed to model the degradation trend and predict the RUL of Lithium-ion batteries while accounting for battery health and operating conditions. The proposed physics-informed long short-term memory (PI-LSTM) model combines a physics-based calendar and cycle aging (CCA) model with an LSTM layer. The CCA model measures the aging effect of Lithium-ion batteries by combining five operating stress factor models. The PI-LSTM uses an LSTM layer to learn the relationship between the degradation trend determined by the CCA model and the online monitoring data of different cycles (i.e., voltage, current, and cell temperature). After the degradation pattern of a battery is estimated by the PI-LSTM model, another LSTM model is then used to predict the future degradation and remaining useful life (RUL) of the battery by learning the degradation trend estimated by the PI-LSTM model. Monitoring data of eleven Lithium-ion batteries under different operating conditions was used to demonstrate the proposed method. Experimental results have shown that the proposed method can accurately model the degradation behavior as well as predict the RUL of Lithium-ion batteries under different operating conditions. 

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2. State-of-Health Estimation for Sustainable Electric Vehicle Batteries Using Temporal-Enhanced Self-Attention Graph Neural Networks

   https://doi.org/10.1115/1.4065146  

   Zhao, Yixin; Behdad, Sara (June 2024, Journal of Energy Resources Technology)

   Abstract Electric vehicles (EVs) have emerged as an environmentally friendly alternative to conventional fuel vehicles. Lithium-ion batteries are the major energy source for EVs, but they degrade under dynamic operating conditions. Accurate estimation of battery state of health is important for sustainability as it quantifies battery condition, influences reuse possibilities, and helps alleviate capacity degradation, which finally impacts battery lifespan and energy efficiency. In this paper, a self-attention graph neural network combined with long short-term memory (LSTM) is introduced by focusing on using temporal and spatial dependencies in battery data. The LSTM layer utilizes a sliding window to extract temporal dependencies in the battery health factors. Two different approaches to the graph construction layer are subsequently developed: health factor-based and window-based graphs. Each approach emphasizes the interconnections between individual health factors and exploits temporal features in a deeper way, respectively. The self-attention mechanism is used to compute the adjacent weight matrix, which measures the strength of interactions between nodes in the graph. The impact of the two graph structures on the model performance is discussed. The model accuracy and computational cost of the proposed model are compared with the individual LSTM and gated recurrent unit (GRU) models. 

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3. Sensorless parameter estimation of vanadium redox flow batteries in charging mode considering capacity fading

   Khaki, Bahman; Das, Pritam (November 2921, Journal of energy storage)

   Uwe Sauer, Dirk (Ed.)

   ABSTRACT State of Charge (SoC) and discharge capacity of the batteries are parameters that cannot be determined directly from the battery monitoring and control system and requires estimation. Current and voltage sensors have inherent error and delay leading to inaccurate measurements leading to inaccurate SoC and discharge capacity estimations. These sensors also have an additional cost to the battery system. This paper proposes a sensorless approach to estimate parameters of Vanadium Redox Flow Batteries (VRFBs) for both CC and CV charging methods by estimating battery current in CV mode and terminal voltage in CC mode. The results of estimations by the sensorless approach show a maximum relative error of 0.0035 in estimating terminal voltage in CC charging and a maximum relative error of 0.045 in estimating charging current in CV mode. Furthermore, long- term operation of vanadium redox flow batteries causes ion diffusions across the membrane and the depletion of active materials, which leads to capacity fading in VRFBs and inaccurate SoC estimation. To address the inaccuracy of SoC estimation in the long-term use of the battery, the capacity fading model is also considered for VRFBs in this paper. Experimental results show a 19% electrolyte volume change in the positive and negative tanks after 200 cycles of charge/discharge due to the bulk electrolyte transfer between the positive and negative sides of the battery system. This change of electrolyte volume results in 13.73% capacity fading after 200 cycles of charging/discharging. The SoC also changes by 7.1% after 200 cycles, due to the capacity and electrolyte volume loss, which shows the necessity of considering capacity fading in long-term use of the battery. 

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4. A Trustable LSTM-Autoencoder Network for Cyberbullying Detection on Social Media Using Synthetic Data

   Akter, M; Shahriar, H; Cuzzocrea, A; Wu, F; Rodriguez-Cardenas, J (December 2023, IEEE Big Data 2023)

   Social media cyberbullying has a detrimental effect on human life. As online social networking grows daily, the amount of hate speech also increases. Such terrible content can cause depression and actions related to suicide. This paper proposes a trustable LSTM Autoencoder Network for cyberbullying detection on social media using synthetic data. We have demonstrated a cutting-edge method to address data availability difficulties by producing machine-translated data. However, several languages such as Hindi and Bangla still lack adequate investigations due to a lack of datasets. We carried out experimental identification of aggressive comments on Hindi, Bangla, and English datasets using the proposed model and traditional models, including Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), LSTM-Autoencoder, Word2vec, Bidirectional Encoder Representations from Transformers (BERT), and Generative Pre-trained Transformer 2 (GPT-2) models. We employed evaluation metrics such as f1-score, accuracy, precision, and recall to assess the models’ performance. Our proposed model outperformed all the models on all datasets, achieving the highest accuracy of 95%. Our model achieves state-of-the-art results among all the previous works on the dataset we used in this paper. 

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5. MULTIFUNCTIONAL CHARACTERIZATION OF 3D PRINTED STRUCTURAL BATTERY COMPOSITES FOR BATTERY HEALTH MONITORING

   https://doi.org/10.12783/shm2023/37000  

   CHEN, YUEKUN; DONG, XIANGYANG (September 2023, Destech Publications, Inc.)

   As a promising lightweight multifunctional material, carbon fiber structural battery composites have great potentials to enable longer service life and operating distance for the rapidly increasing mobile electric technologies. While simultaneously carrying mechanical loads and storing electrical energy, the developed multifunctional composites can achieve “massless” energy storage and further extend to sensing and energy harvesting for self-powered structural health monitoring. However, it is still very challenging to predict the state-of-health of structural battery composites due to a lack of understanding of underlying coupled mechanical-electrochemical phenomena during operation. In this study, we first use a novel 3D printing method to fabricate and tailor microstructure of the multifunctional carbon fiber composites. With an optimal electrode layer thickness of 0.4 mm, the stable specific capacity at 1C reaches over 80% of the theoretical capacity of the electrode active materials (lithium iron phosphate) with an average energy density of 152 Wh/kg observed. The corresponding flexural modulus and flexural strength are 8.7 GPa and 69.6 MPa, respectively. The state-of-health of 3D printed structural battery composites under electrochemical cycling and external mechanical loadings are also investigated. The mechanical performance is not affected by the electrochemical charge-discharge processes. The structural battery composites under three-point bending testing show good capacity retention with rapid degradation of electrochemical performance observed near fracture point. The findings from this study not only provide insights for monitoring the state-of-health of structural battery but also show mechanical-electrochemical coupling as a potential way of self-powered structural health monitoring through the 3D printed multifunctional composites. 

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