Article, 2024

Review of battery state estimation methods for electric vehicles-Part II: SOH estimation

Journal of Energy Storage, ISSN 2352-152X, 2352-1538, Volume 96, Page 112703, 10.1016/j.est.2024.112703

Contributors

Demirci, Osman 0000-0002-8471-1373 (Corresponding author) [1] Taskin, Sezai 0000-0002-2763-1625 [1] Schaltz, Erik 0000-0002-8540-0040 [2] Demirci, Burcu Acar [1]

Affiliations

  1. [1] Manisa Celal Bayar University
    2. [NORA names: Turkey; Asia, Middle East; OECD];
  2. [2] Aalborg University
    3. [NORA names: AAU Aalborg University; University; Denmark; Europe, EU; Nordic; OECD]

Abstract

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.

Keywords

Artificial, Deep, EV batteries, EVs, SOH estimation method, accuracy, adaptation, advances, age, aging characteristics, algorithm, approach, artificial intelligence, artificial neural network, battery, battery aging, battery degradation, battery energy storage system, battery management system, capacity, characteristics, complex models, comprehensive review, conditions, correlation, cycle life, data, data-driven method, dataset, decision, deep learning, degradation, development, discovery, durability, efficiency, electric vehicles, electricity, electrochemical processes, energy storage system, estimation, estimation method, estimation techniques, exploration, external factors, factors, health, historical data, improved accuracy, intelligence, learning, learning algorithms, life, machine, machine learning, machine learning algorithms, maintenance, management system, method, model, model-based approach, network, neural network, operating conditions, operation, operation of EV, overall health, patterns, performance, proactive maintenance, process, review, state, state estimation method, state of health, state of health estimation, state-of-health estimation technique, storage system, study, support, support vector machine, system, technique, temperature, temperature variation, training algorithm, usage, usage patterns, variation, vector machine, vehicle, years

Data Provider: Digital Science

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