Uniqueness of multilayer perceptron-based capacity prediction for contributing state-of-charge estimation in a lithium primary battery

Choi, Changki; Park, Seongyun; Kim, Jong-Hoon

doi:10.1016/j.asej.2022.101936

Cited by 10 publications

(6 citation statements)

References 45 publications

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“…Also, Softmax is a mathematical function that converts a vector of real numbers into a probability distribution. It's often used in machine learning and deep learning models to normalize the output of a neural network, assigning probabilities to multiple classes [172].…”

Section: Advanced Machine Learning Techniquesmentioning

confidence: 99%

“…Real-time SoC estimation is crucial for optimizing EV operation [164]. Future research should focus on developing real-time SoC estimation algorithms that can run efficiently on edge computing platforms [172]. These platforms enable onboard processing of data, reducing the reliance on cloudbased solutions and minimizing latency.…”

Section: E Real-time Implementation and Edge Computingmentioning

confidence: 99%

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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: Advanced Machine Learning Techniquesmentioning

confidence: 99%

Section: E Real-time Implementation and Edge Computingmentioning

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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“…LIB working characteristic.-Marine power LIBs are mainly lithium iron phosphate, which generates current through the movement of lithium ions and electrons inside, at this point the capacity of the battery decreases as the discharge proceeds. 26,27 The battery capacity is considered to have experienced a cycle when the accumulated capacity reaches the rated capacity. Meanwhile, the capacity also decays with the increase in the number of discharges.…”

Section: Lib Working Characteristics and Health Indicatorsmentioning

confidence: 99%

Prediction of Battery SOH and RUL Based on Cooperative Characteristics in Voltage-Temperature-Time Dimensions

Tang,

Zhang,

Ning

et al. 2023

J. Electrochem. Soc.

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In the prognostics health management (PHM) of marine power lithium batteries, the estimation of the state of health (SOH) and the prediction of remaining useful life (RUL) are of great importance to ensure the battery operational safety and efficiency. In this study, an improved multivariate dimensionality-reduction for Bayesian optimized bi-directional long short-term memory (IMD-BiLSTM) algorithm is proposed and applied to realize SOH estimation and RUL prediction of lithium battery. Specifically, based on the discharging data of lithium battery under specific operating conditions, several health indicators are proposed for the work. On this basis, a collaborative dimensionality reduction algorithm based on Pearson correlation and principal component analysis is proposed to further retain feature information and reduce input dimensionality. Then, the prediction model based on BiLSTM is established, in which the hyperparameters are optimized by Bayesian algorithm. Finally, the effectiveness of the proposed IMD-BiLSTM method is verified by experiments based on the NASA PCoE dataset, and the prediction accuracies of SOH and RUL are emphatically analyzed. Numerical simulation results show that the proposed IMD-BiLSTM-method can effectively extract battery health characteristics and achieve dimensionality reduction. In addition, the proposed IMD-BiLSTM-method significantly outperforms the compared state-of-the-art algorithms in SOH/RUL prediction accuracy and robustness.

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“…Nonetheless, the number of RC branches employed determines how accurate this model is; however, the model becomes increasingly difficult for users when the number of RC branches is increased to attain accuracy, affecting parameter identification and SOC estimation [45]. Parameter identification interests many academics due to its significance for various model-based SOC estimations [46,47], such as the neural network, genetic algorithm [48], optimization algorithm, and least square identification [49]. Optimization algorithms are better suited for parameter identification than neural network algorithms due to their simplicity and ease of setting.…”

Section: Literature Reviewmentioning

confidence: 99%

Parameter Identification of Lithium-Ion Battery Model Based on African Vultures Optimization Algorithm

et al. 2023

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This paper establishes a study for an accurate parameter modeling method for lithium-ion batteries. A precise state space model generated from an equivalent electric circuit is used to carry out the proposed identification process, where parameter identification is a nonlinear optimization process problem. The African vultures optimization algorithm (AVOA) is utilized to solve this problem by simulating African vultures’ foraging and navigating habits. The AVOA is used to implement this strategy and improve the quality of the solutions. Four scenarios are considered to take the effect of loading, fading, and dynamic analyses. The fitness function is selected as the integral square error between the estimated and measured voltage in these scenarios. Numerical simulations were executed on a 2600 mAhr Panasonic Li-ion battery to demonstrate the effectiveness of the suggested parameter identification technique. The proposed AVOA was fulfilled with high accuracy, the least error, and high closeness with the experimental data compared with different optimization algorithms, such as the Nelder–Mead simplex algorithm, the quasi-Newton algorithm, the Runge Kutta optimizer, the genetic algorithm, the grey wolf optimizer, and the gorilla troops optimizer. The proposed AVOA achieves the lowest fitness function level of the scenarios studied compared with relative optimization algorithms.

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Uniqueness of multilayer perceptron-based capacity prediction for contributing state-of-charge estimation in a lithium primary battery

Cited by 10 publications

References 45 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

Prediction of Battery SOH and RUL Based on Cooperative Characteristics in Voltage-Temperature-Time Dimensions

Parameter Identification of Lithium-Ion Battery Model Based on African Vultures Optimization Algorithm

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