Effective cell balancing is crucial for optimizing the performance, lifespan, and safety of lithium-ion batteries in electric vehicles (EVs). This study explores various cell balancing methods, including passive techniques (switching shunt resistor) and active techniques multiple-inductor, flyback converter, and single capacitor), using MATLAB Simulink. The objective is to identify the most
To enhance the performance and cost-effectiveness of batteries, accurate estimation of their state of health (SOH) and reliable lifetime predictions under various operating conditions are crucial [2].
Furthermore, there are gaps in research in terms of explaining in depth the complex process of battery aging and understanding the interaction between operating conditions, testing practices, and battery performance. The process of battery disintegration is gradual as the number of battery cycles increases. The discharge process is more
In this research work, model development and assessment are investigated focusing on the less complex and performance-based methodologies, which include the semi-empirical approach and ML algorithms.
Electric vehicle (EV) battery technology is at the forefront of the shift towards sustainable transportation. However, maximising the environmental and economic benefits of
Researchers reviewed the literature on the various methods used around the world to characterize the performance of lithium-ion batteries to provide insight on best practices. Their results may
Lithium-ion power batteries have become integral to the advancement of new energy vehicles. However, their performance is notably compromised by excessive temperatures, a factor intricately linked to the batteries'' electrochemical properties. To optimize lithium-ion battery pack performance, it is imperative to maintain temperatures within an appropriate
It also discussed a critical survey of EV batteries and the importance of efficient battery management strategies, battery modeling and battery SoC estimation in optimizing the performance, longevity, and safety of batteries in EVs. This review served as a valuable resource for understanding the key aspects of battery technology and management in the context of EV
There are three broad classes of methods for estimating SOH: (1) Using mathematics, construct empirical and probabilistic models to simulate the decay process. (2)
In this Review, we examine the latest advances in non-destructive characterization techniques, including electrical sensors, optical fibres, acoustic transducers, X
This research work signifies the importance of lifetime methodological choice and model performance in understanding the complex and nonlinear Li-ion battery aging behavior.
The dynamic and static research is determined by vehicle crash homologation, accreditation requirements, and transport legislation. The safety performance of the EV relies on the safety performance of the battery pack under different environments. Thereby, research on battery pack safety is considered seriously in recent years (Li et al., 2017).
There are three broad classes of methods for estimating SOH: (1) Using mathematics, construct empirical and probabilistic models to simulate the decay process. (2) Physical and electrochemical procedures such as equivalent circuit models (ECM), filters.
Research studies on phase change material cooling and direct liquid cooling for battery thermal management are comprehensively reviewed over the time period of 2018–2023. This review discusses
Lithium-ion batteries, due to their high energy and power density characteristics, are suitable for applications such as portable electronic devices, renewable energy systems, and electric vehicles. Since the charging method
This paper proposes a quantitative methodology to assess battery technologies, based on nine indicators. The performance indicators are measured by means of the proposed experimental design. Besides the comparative methodology, this contribution has as second outcome a general aging model that allows a comprehensive analysis of stress factors
In this research work, model development and assessment are investigated focusing on the less complex and performance-based methodologies, which include the semi-empirical approach and ML algorithms. The objective is to evaluate the developed data-driven model methodologies that are constructed from a commercial cell aging dataset and can be
To enhance the performance and cost-effectiveness of batteries, accurate estimation of their state of health (SOH) and reliable lifetime predictions under various
This work is supported, in part, by the Fulbright postdoctoral research Fellowship, Total S.A./Saft Batteries, under award No. 048589-001 and the National Science Foundation AI Institute for
This research work signifies the importance of lifetime methodological choice and model performance in understanding the complex and nonlinear Li-ion battery aging behavior.
Electric vehicle (EV) battery technology is at the forefront of the shift towards sustainable transportation. However, maximising the environmental and economic benefits of electric vehicles depends on advances in battery life cycle management. This comprehensive review analyses trends, techniques, and challenges across EV battery development, capacity
Enhancing battery performance hinges on a deep understanding of their operational and degradation mechanisms, from material composition and electrode structure to large-scale pack integration, necessitating advanced characterization methods. These methods not only enable improved battery performance but also facilitate early detection of substandard
This is a critical review of artificial intelligence/machine learning (AI/ML) methods applied to battery research. It aims at providing a comprehensive, authoritative, and critical, yet easily unde...
Effective cell balancing is crucial for optimizing the performance, lifespan, and safety of lithium-ion batteries in electric vehicles (EVs). This study explores various cell balancing methods,
Artificial intelligence methods employed in battery state prediction stand out with their ability to analyze extensive datasets with high accuracy. These methods offer significant advantages in adapting to changes in battery performance, effectively evaluating complex datasets, and predicting charge status with high accuracy. However, drawbacks
Enhancing battery performance hinges on a deep understanding of their operational and degradation mechanisms, from material composition and electrode structure
In this Review, we examine the latest advances in non-destructive characterization techniques, including electrical sensors, optical fibres, acoustic transducers, X-ray-based imaging and thermal...
This is a critical review of artificial intelligence/machine learning (AI/ML) methods applied to battery research. It aims at providing a comprehensive, authoritative, and critical, yet easily unde...
Accurate forecasting of lithium-ion battery performance is essential for easing consumer concerns about the safety and reliability of electric vehicles. Most research on battery health prognostics
To enhance the performance and cost-effectiveness of batteries, accurate estimation of their state of health (SOH) and reliable lifetime predictions under various operating conditions are crucial .
Several international initiatives have been created to develop novel tools and protocols for reducing the number of experiments in battery research by a factor of 3, (7) and, more generally, for boosting the pace of material discovery for energy applications by a factor of ∼10.
Based on the analyzed articles, probability estimation approaches constitute the most used methods (27%), followed by support vector and NN families (both 23%), linear approaches (14%), ensemble learning (9%), and DT-based algorithms (4%). Zhu et al. (338) used a set of DT algorithms to analyze the lifetime of batteries.
Through the exploration of various methods' strengths and weaknesses and by advocating a hybrid approach, it aims to contribute significantly to battery prognostics and bolster the development of accurate and reliable lifetime prediction models.
By analyzing patterns and anomalies in battery usage and condition data, these systems can forecast future battery health status, enabling timely interventions. The real-time aspect of these predictions is crucial for dynamic environments where battery performance directly impacts the overall functionality of the device.
Application to Battery Cell Diagnosis and Prognosis The prediction of the battery performance and lifetime as well as the identification of the main sources of battery performance limitations and aging are major concerns while integrating batteries in applications, such as electric vehicles (EVs).
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