The value of thermal management control strategies for battery energy storage in grid decarbonization: Issues and recommendations

Hannan, M. A.; Al-Shetwi, Ali Q.; Begum, Rawshan Ara; Young, Stanley; Hoque, Mohammed Mozammel; Ker, PinJern; Mansur, M.; Alzaareer, Khaled

doi:10.1016/j.jclepro.2020.124223

Cited by 41 publications

(19 citation statements)

References 118 publications

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“…Typically, trials with a 1 Hz sample frequency are used to collect data. The data duration between EV driving cycles varies with different voltage and current levels [127]. For instance, one EV drive cycle is predicted to take 1372 s, 360 s, 916 s, and 600 s, respectively, by the federal urban driving schedule (FUDS), dynamic stress test (DST), Beijing dynamic stress test (BJDST), and US06 drive cycle [128].…”

Section: Data Abundance and Varietymentioning

confidence: 99%

Artificial Intelligence Approaches for Advanced Battery Management System in Electric Vehicle Applications: A Statistical Analysis towards Future Research Opportunities

Lipu,

Miah,

Jamal

et al. 2023

Vehicles

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In order to reduce carbon emissions and address global environmental concerns, the automobile industry has focused a great deal of attention on electric vehicles, or EVs. However, the performance and health of batteries can deteriorate over time, which can have a negative impact on the effectiveness of EVs. In order to improve the safety and reliability and efficiently optimize the performance of EVs, artificial intelligence (AI) approaches have received massive consideration in precise battery health diagnostics, fault analysis and thermal management. Therefore, this study analyzes and evaluates the role of AI approaches in enhancing the battery management system (BMS) in EVs. In line with that, an in-depth statistical analysis is carried out based on 78 highly relevant publications from 2014 to 2023 found in the Scopus database. The statistical analysis evaluates essential parameters such as current research trends, keyword evaluation, publishers, research classification, nation analysis, authorship, and collaboration. Moreover, state-of-the-art AI approaches are critically discussed with regard to targets, contributions, advantages, and disadvantages. Additionally, several significant problems and issues, as well as a number of crucial directives and recommendations, are provided for potential future development. The statistical analysis can guide future researchers in developing emerging BMS technology for sustainable operation and management in EVs.

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Section: Data Abundance and Varietymentioning

confidence: 99%

Artificial Intelligence Approaches for Advanced Battery Management System in Electric Vehicle Applications: A Statistical Analysis towards Future Research Opportunities

Lipu,

Miah,

Jamal

et al. 2023

Vehicles

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“…A real velocity profile of a 12 m urban transient bus in Berlin-Germany was recorded by simultaneously using a Global Navigation Satellite System sensor. Besides this velocity profile, the battery state of charge (SOC) and auxiliary power were measured to implement the energy consumption model [61,62]. By using Internet of Things (IoT) technology integrated into the Global Positioning System, six BEBs were measured in three bus lines located in southern Finland to accurately estimate the BEB energy consumption.…”

Section: Required Data Of Actual-measurement Equipmentmentioning

confidence: 99%

Review of the Estimation Methods of Energy Consumption for Battery Electric Buses

Al-Ogaili¹,

Al-Shetwi

Al‐Masri

et al. 2021

Energies

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In the transportation sector, electric battery bus (EBB) deployment is considered to be a potential solution to reduce global warming because no greenhouse gas (GHG) emissions are directly produced by EBBs. In addition to the required charging infrastructure, estimating the energy consumption of buses has become a crucial precondition for the deployment and planning of electric bus fleets. Policy and decision-makers may not have the specific tools needed to estimate the energy consumption of a particular bus network. Therefore, many state-of-the-art studies have proposed models to determine the energy demand of electric buses. However, these studies have not critically reviewed, classified and discussed the challenges of the approaches that are applied to estimate EBBs’ energy demands. Thus, this manuscript provides a detailed review of the forecasting models used to estimate the energy consumption of EBBs. Furthermore, this work fills the gap by classifying the models for estimating EBBs’ energy consumption into small-town depot and big-city depot networks. In brief, this review explains and discusses the models and formulations of networks associated with well-to-wheel (WTW) assessment, which can determine the total energy demand of a bus network. This work also reviews a survey of the most recent optimization methods that could be applied to achieve the optimal pattern parameters of EBB fleet systems, such as the bus battery capacity, charger rated power and the total number of installed chargers in the charging station. This paper highlights the issues and challenges, such as the impact of external factors, replicating real-world data, big data analytics, validity index, and bus routes’ topography, with recommendations on each issue. Also, the paper proposes a generic framework based on optimization algorithms, namely, artificial neural network (ANN) and particle swarm optimization (PSO), which will be significant for future development in implementing new energy consumption estimation approaches. Finally, the main findings of this manuscript further our understanding of the determinants that contribute to managing the energy demand of EBBs networks.

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“…The emerging role of AI for RE utilization may help it achieve some targets (22%) within the economy group because (a) the AI, machine learning, and smart communication can be used to succeed this harmonization effort to have unique standards and requirements concerning RE integration around the world 66,67 (target 17.14 and 17.6); (b) improvement and optimization (using AI) will enhance the RE efficiency and production 68,69 (target 12.11); (c) optimization of the west to energy (WtE) technologies for treating of various waste fractions in a medium-term future energy system considering their complex properties and optimizing both investments and production. Optimization of routes, which include waste components (i.e., food and yard wastes, non-biodegradable components, rubber, plastic, textile, leather, and wood) are the optimized WtE routes for maximum power generation potential by biochemical and thermochemical treatments of solid waste 70,71 (targets 12.4, 12.5); and (d) the AI, machine learning, optimization, and smart communications have positive impact to increase RE productivity, reduce the cost, and introduce innovation toward smart grid [72][73][74] (target 8.1). Furthermore, fewer targets within the Economy group (6 targets, 10%) can be impacted negatively by RE.…”

Section: Re Utilization and Economic Outcomesmentioning

confidence: 99%

Impact of Renewable Energy Utilization and Artificial Intelligence in Achieving Sustainable Development Goals

Hannan

Al-Shetwi

Begum

et al. 2021

Preprint

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Many countries around the world are planning to reach 100% renewable energy (RE) use by 2050. In this context and due to the recent sharp increase in RE utilization in the global energy mix along with its progressive impact on the world energy sector, the evaluation and investigation of its effect on achieving sustainable development goals (SDGs) are not covered sufficiently. Here, we present an assessment of the emerging role of RE utilization and artificial intelligence (AI) toward achieving SDGs. A total of 17 SDGs were divided into three groups, namely, environment, society, and economy, as per the three key pillars of sustainable development. The RE has a positive impact toward achieving 75 targets across all SDGs by using an expert elicitation method-based consensus. However, it may negatively affect the accomplishment of the 27 targets. In addition, the AI can help the RE to enable the attainment of 42 out of 169 targets. With the current exponential growth of RE share and AI development together with addressing certain present limitations, this impact may cover additional targets in the future. Nevertheless, the present research foci neglect significant facets. The exponential growth of RE share and rapid evolution of AI need to be accompanied through the requisite regulatory insight and technology regulation to cover additional targets in the future.

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The value of thermal management control strategies for battery energy storage in grid decarbonization: Issues and recommendations

Cited by 41 publications

References 118 publications

Artificial Intelligence Approaches for Advanced Battery Management System in Electric Vehicle Applications: A Statistical Analysis towards Future Research Opportunities

Artificial Intelligence Approaches for Advanced Battery Management System in Electric Vehicle Applications: A Statistical Analysis towards Future Research Opportunities

Review of the Estimation Methods of Energy Consumption for Battery Electric Buses

Impact of Renewable Energy Utilization and Artificial Intelligence in Achieving Sustainable Development Goals

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