Artificial Neural Networks, Gradient Boosting and Support Vector Machines for electric vehicle battery state estimation: A review

A Manoharan, KM Begam, VR Aparow… - Journal of Energy …, 2022 - Elsevier
Abstract In recent years, Artificial Intelligence has been widely used for determining the
current state of Li-ion batteries used for Electric Vehicle applications. It is crucial to have an …

Review on state of charge estimation techniques of lithium-ion batteries: A control-oriented approach

N Ghaeminezhad, Q Ouyang, J Wei, Y Xue… - Journal of Energy …, 2023 - Elsevier
Energy storage has become one of the most critical issues of modern technology. In this
regard, lithium-ion batteries have proven effective as an energy storage option. To optimize …

Lithium-ion battery state of health estimation using a hybrid model based on a convolutional neural network and bidirectional gated recurrent unit

Y Mazzi, HB Sassi, F Errahimi - Engineering Applications of Artificial …, 2024 - Elsevier
This paper proposes a real-time state of health (SOH) estimation model based on a deep
learning (DL) framework. The proposed model is a combination of two different …

[HTML][HTML] Artificial intelligence approaches for advanced battery management system in electric vehicle applications: A statistical analysis towards future research …

MSH Lipu, MS Miah, T Jamal, T Rahman, S Ansari… - Vehicles, 2023 - mdpi.com
In order to reduce carbon emissions and address global environmental concerns, the
automobile industry has focused a great deal of attention on electric vehicles, or EVs …

State of charge estimation of Li-ion batteries based on deep learning methods and particle-swarm-optimized Kalman filter

M Li, C Li, Q Zhang, W Liao, Z Rao - Journal of Energy Storage, 2023 - Elsevier
The estimation of SOC is a key issue for the high-efficient and reliable operation of Li-ion
batteries, thus has been increasingly concerned in current years with the development of …

A case study of a tiny machine learning application for battery state-of-charge estimation

S Giazitzis, M Sakwa, S Leva, E Ogliari, S Badha… - Electronics, 2024 - mdpi.com
Growing battery use in energy storage and automotive industries demands advanced
Battery Management Systems (BMSs) to estimate key parameters like the State of Charge …

EdgeCog: A real-time bearing fault diagnosis system based on lightweight edge computing

L Fu, K Yan, Y Zhang, R Chen, Z Ma… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Deep learning has made important contributions to classification tasks applied to fault
diagnosis. However, it is crucial to integrate the technologies into real industrial applications …

A soft actor-critic reinforcement learning framework for optimal energy management in electric vehicles with hybrid storage

Y Mazzi, HB Sassi, F Errahimi, N Es-Sbai - Journal of Energy Storage, 2024 - Elsevier
The efficient energy management of electric vehicles (EVs) equipped with hybrid energy
storage systems (HESS) poses a significant challenge due to its vast search space …

Metaheuristics‐optimized deep learning to predict generation of sustainable energy from rooftop plant microbial fuel cells

JS Chou, TC Cheng, CY Liu… - International Journal of …, 2022 - Wiley Online Library
Plant microbial fuel cells (PMFCs) are an emergent green‐energy technology that
continuously converts solar energy into electricity. Placing PMFCs on the roofs of urban …

A new lithium polymer battery dataset with different discharge levels: SOC estimation of lithium polymer batteries with different convolutional neural network models

G Taş, A Uysal, C Bal - Arabian Journal for Science and Engineering, 2023 - Springer
In this study, a new dataset was created for use to estimate the state of charge (SOC) of
lithium polymer batteries. A new experimental system was created to obtain the dataset by …