Electric Vehicle Battery Technologies and Capacity Prediction: A Comprehensive Literature Review of Trends and Influencing Factors DOI Creative Commons

Vo Tri Duc Sang,

Quang Huy Duong, Li Zhou

et al.

Batteries, Journal Year: 2024, Volume and Issue: 10(12), P. 451 - 451

Published: Dec. 19, 2024

Electric vehicle (EV) battery technology is at the forefront of shift towards sustainable transportation. However, maximising environmental and economic benefits electric vehicles depends on advances in life cycle management. This comprehensive review analyses trends, techniques, challenges across EV development, capacity prediction, recycling, drawing a dataset over 22,000 articles from four major databases. Using Dynamic Topic Modelling (DTM), this study identifies key innovations evolving research themes battery-related technologies, degradation factors, recycling methods. The literature structured into two primary themes: (1) “Electric Vehicle Battery Technologies, Development & Trends” (2) “Capacity Prediction Influencing Factors”. DTM revealed pivotal findings: advancements lithium-ion solid-state batteries for higher energy density, improvements technologies to reduce impact, efficacy machine learning-based models real-time prediction. Gaps persist scaling methods, developing cost-effective manufacturing processes, creating standards impact assessment. Future directions emphasise multidisciplinary new chemistries, efficient end-of-life management, policy frameworks that support circular economy practices. serves as resource stakeholders address critical technological regulatory will shape future vehicles.

Language: Английский

Analyzing the Adoption of Hybrid Electric and Hydrogen Vehicles in Indonesia: A Multi-Criteria and Total Cost of Ownership Approach DOI Creative Commons

Hendri Bhirowo,

Indrawati Indrawati,

Handrea Bernando Tambunan

et al.

Cleaner Engineering and Technology, Journal Year: 2025, Volume and Issue: unknown, P. 100893 - 100893

Published: Jan. 1, 2025

Language: Английский

Citations

0
Influence of uncertainties in a battery pack with air cooling for electric vehicles on temperature difference and volume of battery module DOI
Anshu Sharma, Neeraj Kumar Shukla, Aman Garg

et al.

Journal of Energy Storage, Journal Year: 2025, Volume and Issue: 113, P. 115643 - 115643

Published: Feb. 1, 2025

Language: Английский

Citations

0
Tube-Based Robust Nonlinear Model Predictive Control for Thermal Processes with Variable with Long-Time Delay DOI
Katherine Aro, Óscar Camacho,

Marco Luis Herrera

et al.

Published: Jan. 1, 2025

Language: Английский

Citations

0
Experimental Analysis of Battery Thermal Management Techniques for Electric Vehicle Lithium-Ion Batteries Using MATLAB Simulink, Simscape, and Stateflow Simulations DOI

Shanmuganathan Thangaraju,

N. Meenakshi,

M. Ganesan

et al.

SAE technical papers on CD-ROM/SAE technical paper series, Journal Year: 2025, Volume and Issue: 1

Published: April 1, 2025

<div class="section abstract"><div class="htmlview paragraph">The use of lithium-ion batteries in electric vehicles marks a major progression the automotive sector. Energy storage systems extensively make these batteries. The extended life cycle, low self-discharge rates, high energy density, and eco-friendliness are well-known. However, Temperature sensitivity has an adverse effect on battery safety, durability, performance. Thus, maintaining ideal operating conditions reducing chance thermal runaway depend heavily efficient management. To address this, experimental study was conducted various management techniques, including active, passive, hybrid approaches. These techniques were investigated for their cooling efficiencies under different conditions. electro-thermal behavior cylindrical cells, packs, supervisory control simulated using MATLAB Simulink, Simscape, Stateflow. This conductivity Liquid Cooling (LC), Air (AC), Heat Pipe (HP), Phase Change Material (PCM) evaluated performance both individual according to C rate battery. Simulation results analyzed high-power charging discharging typical vehicles. investigation identified that, active can reduce temperature rise during deep cycle. not all driving situations environmental factors call cooling. For modest vehicle speeds regular ambient temperatures, passive is adequate. analysis indicates strategies offer better trade-off between efficiency effective depending runtime requirements.</div></div>

Language: Английский

Citations

0
Research on Energy-Saving Control Strategy of Nonlinear Thermal Management System for Electric Tractor Power Battery Under Plowing Conditions DOI Creative Commons
Xiaoshuang Guo,

Ruiliang Xu,

Junjiang Zhang

et al.

World Electric Vehicle Journal, Journal Year: 2025, Volume and Issue: 16(5), P. 249 - 249

Published: April 25, 2025

To address the issue of over-regulation temperature a liquid-cooled power battery thermal management system under plowing condition electric tractors, which leads to high energy consumption, nonlinear model prediction control (NMPC) algorithm for tractors applicable is proposed. Firstly, control-oriented tractor heat production and transfer were established based on operating conditions Bernardi’s theory production. Secondly, in order improve accuracy prediction, method future working information moving average Finally, predictive cooling optimization strategy proposed, with objectives quickly achieving regulation reducing compressor consumption. The proposed validated by simulation hardware-in-the-loop (HIL) testbed. results show that NMPC can better, holding phase reduces speed variation range 24.6% compared PID, it consumption 23.1% whole phase.

Language: Английский

Citations

0
A Critical Review of Advancements and Challenges in Thermal Management Systems For Lithium-Ion Batteries DOI

Chun Yang Guo,

Mohammed W. Muhieldeen, Kah Hou Teng

et al.

Journal of Advanced Research in Numerical Heat Transfer, Journal Year: 2025, Volume and Issue: 35(1), P. 117 - 161

Published: May 6, 2025

Battery thermal management systems (BTMS) ensure the safety and performance of lithium-ion batteries, which power electric vehicles. However, designing an effective BTMS is challenging due to batteries' complex behaviour sensitivity temperature variations. This review comprehensively explores current vital technologies trends in BTMS, explicitly focusing on analysing various cooling control strategies. To discuss four primary technologies: air cooling, liquid immersion phase change material (PCM) cooling. The advantages disadvantages each technology are compared terms cost-effectiveness, applicability, limitations when dealing with high-energy-density batteries. Furthermore, delves into discussion strategies data prediction methods for emphasizing importance advanced analysis optimising battery safety. Different strategies, such as passive, active, hybrid control, introduced evaluated. Data methods, artificial neural networks, fuzzy logic, machine learning, also presented discussed. comprehensive provides in-depth understanding while serving a valuable reference future research application.

Language: Английский

Citations

0
Enhancing Fire Protection in Electric Vehicle Batteries Based on Thermal Energy Storage Systems Using Machine Learning and Feature Engineering DOI Creative Commons

Mahmoud M. Kiasari,

Hamed H. Aly

Fire, Journal Year: 2024, Volume and Issue: 7(9), P. 296 - 296

Published: Aug. 23, 2024

Thermal Energy Storage (TES) plays a pivotal role in the fire protection of Li-ion batteries, especially for high-voltage (HV) battery systems Electrical Vehicles (EVs). This study covers application TES mitigating thermal runaway risks during different charging/discharging conditions known as Vehicle-to-grid (V2G) and Grid-to-vehicle (G2V). Through controlled simulations Simulink, this research models real-world scenarios to analyze effectiveness controlling under various environmental conditions. also integrates Machine Learning (ML) techniques utilize produced data by simulation model predict any probable spikes enhance system reliability, focusing on crucial factors like temperature, current, or State charge (SoC). Feature engineering is employed identify key parameters among all features that are considered study. For broad comparison models, three ML techniques, logistic regression, support vector machine (SVM), Naïve Bayes, have been used alongside their hybrid combination determine most accurate one related topic. concludes SoC significant factor affecting management while grid power consumption has least impact. Additionally, findings demonstrate regression outperforms other methods, with improving feature be it can increase efficiency due its linearity capture capability.

Language: Английский

Citations

2
Electric Vehicle Battery Technologies and Capacity Prediction: A Comprehensive Literature Review of Trends and Influencing Factors DOI Creative Commons

Vo Tri Duc Sang,

Quang Huy Duong, Li Zhou

et al.

Batteries, Journal Year: 2024, Volume and Issue: 10(12), P. 451 - 451

Published: Dec. 19, 2024

Electric vehicle (EV) battery technology is at the forefront of shift towards sustainable transportation. However, maximising environmental and economic benefits electric vehicles depends on advances in life cycle management. This comprehensive review analyses trends, techniques, challenges across EV development, capacity prediction, recycling, drawing a dataset over 22,000 articles from four major databases. Using Dynamic Topic Modelling (DTM), this study identifies key innovations evolving research themes battery-related technologies, degradation factors, recycling methods. The literature structured into two primary themes: (1) “Electric Vehicle Battery Technologies, Development & Trends” (2) “Capacity Prediction Influencing Factors”. DTM revealed pivotal findings: advancements lithium-ion solid-state batteries for higher energy density, improvements technologies to reduce impact, efficacy machine learning-based models real-time prediction. Gaps persist scaling methods, developing cost-effective manufacturing processes, creating standards impact assessment. Future directions emphasise multidisciplinary new chemistries, efficient end-of-life management, policy frameworks that support circular economy practices. serves as resource stakeholders address critical technological regulatory will shape future vehicles.

Language: Английский

Citations

1