An electromechanical coupling model-based state of charge estimation method for lithium-ion pouch battery modules

Jiang, Yihui; Liu, Mengmeng; Mei, Xuesong

doi:10.1016/j.energy.2022.125019

Cited by 13 publications

(3 citation statements)

References 34 publications

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“…EVs are pioneering solutions in the realm of sustainable and efficient transportation, ushering in a new era of eco-conscious mobility. EVs ingeniously combine traditional ICEs with electric propulsion systems, culminating in vehicles that offer remarkable advantages such as reduced emissions, improved fuel efficiency, and a reduced carbon footprint [23][24][25]. In contrast to HEVs, where an ICE works in conjunction with an electric motor, EVs rely exclusively on an electric motor for propulsion, utilizing electrical energy as the sole power source, resulting in zero emissions.…”

Section: A Fundamental Of Evsmentioning

confidence: 99%

Advancing State of Charge Management in Electric Vehicles With Machine Learning: A Technological Review

Mousaei,

Naderi,

Bayram

2024

IEEE Access

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As the share of electric vehicles increases, electric vehicles are exposed to broader of driving conditions (e.g., extreme weather), which reduce the performance and driving ranges of electric vehicles below their nameplate rating. To ensure customer confidence and support steady growth in electric vehicle adoption rates, accurate estimation of battery state of charge and maintaining battery state of health through optimal charge/discharge decisions are critical. Recently, vehicle manufacturers have begun to employ machine learning techniques to improve state-of-charge management to better inform drivers about both short-term (state of charge) and long-term (state of health) performance of their vehicles. This comprehensive review article explores the intersection of machine learning and state of charge management in electric vehicles. Recognizing the critical importance of state of charge in optimizing electric vehicle performance, the article starts by evaluating traditional state of charge estimation methods. Subsequently, it delves into the transformative impact of machine learning techniques and associated algorithms on state of charge management. Through the lens of various case studies, this article demonstrates how machine learning-based state of charge estimation empowers electric vehicles to make informed and dynamic energy usage decisions, enhancing efficiency and extending battery life. The challenges of data availability, model interpretability, and real-time processing constraints are acknowledged as impediments to the widespread adoption of machine learning techniques. Despite these challenges, the future outlook for machine learning in state of charge management appears promising, with emerging trends such as deep learning and reinforcement learning poised to refine state of charge estimation accuracy. Moreover, this study sheds light on the transformative potential of machine learning in enhancing state of charge management efficiency and effectiveness for electric vehicles, offering critical insights.. Machine learning emerges as a game-changing force in state of charge management for electric vehicles, paving the way for intelligent and adaptive vehicles that are both environmentally friendly and efficient. This evolving field invites further research and development, making it a vital and exciting area within the automotive industry.

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Section: A Fundamental Of Evsmentioning

confidence: 99%

Advancing State of Charge Management in Electric Vehicles With Machine Learning: A Technological Review

Mousaei,

Naderi,

Bayram

2024

IEEE Access

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“…Jiang et al also considered stress measurement to train LSTM for SoC estimation [10]. Preliminary results revealed that the integration of mechanical measurements with electrical ones leads to improved efficiency when ensuring accuracy, however, further developments need to be addressed, such as optimization of hyperparameters and the use of a dataset with multiple temperatures.…”

Section: Analysis Of Some Of the Latest Contributions Available In Th...mentioning

confidence: 99%

Estimation of the State of Charge/Health of Electric Vehicle Batteries Through Machine Learning Approach

Clemente,

Giovanni,

Ottorino

et al. 2024

Preprint

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Ensuring high performances and lifetime of battery packs has critical importance, because of the transition toward electric mobility. Therefore, correct estimation of the battery state with ad-hoc designed Battery Management Systems (BMS) is pivotal to address this challenge. In this context, application of Machine Learning (ML) is gaining increasing research interest as it includes data-driven algorithms that enable accurate and fast predictions of the battery state. For this reason, this paper aims to contribute with: (i) a survey of the newest contributions to the prediction of the State of Charge/Health (SoC/SoH), and (ii) by schematizing a methodology that uses simulated data to train state-of-the-art types of neural networks (NNs) for SoC and SoH estimation of a LiNMC battery cell. Research papers considered in this review included applications of deep NN, and other ML algorithms. The impact of the training dataset on the performances of the ML models and their capability to generalize is remarked throughout the paper. For this reason, a validated electro-thermal model is used to generate data that accounts for different temperatures and current loads to simulate scenarios with different environmental conditions and driving cycles.

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“…FC is important and will directly affect the capability of the prediction model. The calculation of the Pearson correlation coefficient (PCC) and Spearman correlation coefficient is the most commonly used analysis method 16 . However, these methods only reflect linear and monotonic relationships and may overlook deeper nonlinear correlations.…”

Section: Feature Combination Screeningmentioning

confidence: 99%

A novel feature combination screening method for Li-ion battery state of health prediction

Pei,

Chen,

Chen

et al. 2023

Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023)

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Accurately predicting the state of health (SOH) of Li-ion batteries is beneficial for application management. Here, we propose an optimization scheme for feature combination (FC) to enhance the performance of the SOH prediction model. In this article, we first extract features from the partial charging voltage data of the battery for model input. Then, the Pearson correlation coefficient is combined with a machine learning model to optimize the selection of potential FCs and obtain the optimal FC. Finally, the optimal FC is applied to the SOH prediction tasks. The proposed scheme achieved significant performance improvement in multiple testing tasks and outperformed other methods, demonstrating its effectiveness and superiority.

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An electromechanical coupling model-based state of charge estimation method for lithium-ion pouch battery modules

Cited by 13 publications

References 34 publications

Advancing State of Charge Management in Electric Vehicles With Machine Learning: A Technological Review

Advancing State of Charge Management in Electric Vehicles With Machine Learning: A Technological Review

Estimation of the State of Charge/Health of Electric Vehicle Batteries Through Machine Learning Approach

A novel feature combination screening method for Li-ion battery state of health prediction

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