Browsing by Subject "State estimation methods"

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    Review of battery state estimation methods for electric vehicles - Part I: SOC estimation
    (Elsevier Ltd, 2024) Demirci O.; Taskin S.; Schaltz E.; Acar Demirci B.
    This study presents a comprehensive review of State of Charge (SOC) estimation methods for Lithium-Ion (Li-Ion) batteries, with a specific focus on Electric Vehicles (EVs). The growing interest in EVs and the need for efficient battery management have driven advancements in SOC estimation techniques. Various approaches, including data-driven techniques, advanced filtering methods, and machine learning algorithms have been explored to enhance SOC estimation accuracy. The integration of artificial intelligence and hybrid models has shown promising results in improving SOC estimation performance. However, challenges remain in dealing with non-linear battery behavior, temperature variations, and diverse operating conditions. Researchers are continuously studying to improve the robustness and adaptability of SOC estimation methods to address these challenges. The primary objective of this study is to provide an up-to-date summary of the latest advancements in SOC estimation, offering insights into innovative approaches and developments in this field. All existing SOC methods, their advantages, challenges, and usage rates have been comprehensively examined with a specific focus on EV battery management systems. As the EV market continues to expand, accurate SOC estimation will remain essential for optimal battery management and overall EV performance. Future research will focus on refining existing algorithms, exploring new data-driven techniques, and integrating advanced sensor technologies to achieve real-time and reliable SOC estimation in EVs. © 2024 Elsevier Ltd
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    Review of battery state estimation methods for electric vehicles-Part II: SOH estimation
    (Elsevier Ltd, 2024) Demirci O.; Taskin S.; Schaltz E.; Acar Demirci B.
    State of Health (SOH) significantly determines the performance and durability of EV batteries, with Battery Management System (BMS) playing a crucial role in enhancing their efficiency and operational cycle life. This comprehensive review, the second part of our series on Battery State Estimation Methods for Electric Vehicles, provides an in-depth exploration of SOH estimation methods. SOH, which encompasses a battery's overall health, capacity, and aging characteristics, plays a fundamental role in making informed decisions, conducting proactive maintenance, and ensuring the safe and reliable operation of EVs. Diverse SOH estimation methods, ranging from data-driven to model-based approaches, address the multifaceted challenges associated with battery aging, including electrochemical processes, temperature variations, usage patterns, and external factors. In recent years, data-driven methods, especially those rooted in machine learning and artificial intelligence, have gained prominence. These methods facilitate the discovery of complex models and correlations, encompassing battery degradation and using datasets to train algorithms. Machine learning algorithms including Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Deep Learning (DL), have shown significant promise in estimating SOH by learning from historical data and adapting to varying operational conditions. The studies highlighted in this review demonstrate significant advancements in SOH estimation techniques, leading to improved accuracy, efficiency, and adaptability. These advances contribute to the development of more reliable BMSs for EVs and battery energy storage systems. © 2024 Elsevier Ltd