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Volume 16
Issue 6
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World Electr. Veh. J., Volume 16, Issue 6 (June 2025) – 2 articles

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25 pages, 11933 KiB  
Article
BL-DATransformer Lifespan Degradation Prediction Model of Fuel Cell Using Relative Voltage Loss Rate Health Indicator
by Yinjie Xu, Jing Wang, Donghai Hu, Dagang Lu, Xiaoyan Zhang, Wenxuan Wei, Hua Ding and Shupei Zhang
World Electr. Veh. J. 2025, 16(6), 290; https://doi.org/10.3390/wevj16060290 - 22 May 2025
Abstract
The durability of fuel cells is the main obstacle to their large-scale application. Deep learning-based methods improve the accuracy of fuel cell lifespan degradation prediction. However, their reliance on static health indicators and application in bench experiment environments limits their ability to capture [...] Read more.
The durability of fuel cells is the main obstacle to their large-scale application. Deep learning-based methods improve the accuracy of fuel cell lifespan degradation prediction. However, their reliance on static health indicators and application in bench experiment environments limits their ability to capture degradation trends under dynamic conditions. This paper proposes a novel lifespan degradation prediction method for fuel cells operating in real-world traffic environments, utilizing Relative Voltage Loss Rate (RVLR) as the health indicator. Initially, fuel cell lifespan degradation data with varying characteristics are obtained through a dynamic bench experiment and two sets of road driving experiments. Subsequently, a lifespan degradation prediction model based on the Bidirectional Long Short-Term Memory Dual-Attention Transformer (BL-DATransformer) is proposed. An ablation study is conducted on this architecture, with analysis performed to evaluate the influence of diverse input features on model performance. Finally, the comparison results with LSTM, Transformer, and Informer indicate that under smooth traffic conditions, when the training length is 70%, the RMSE is reduced by 84.32%, 74.94%, and 18.49%, respectively. Under congested traffic conditions, with the same training length, the RMSE is reduced by 88.30%, 78.33%, and 26.52%, respectively. The result demonstrates that the prediction method has high accuracy and practical application value. Full article
(This article belongs to the Special Issue Revolutionizing the Automotive Landscape: Fuel Cell Applications Powering the Future)
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14 pages, 3842 KiB  
Article
Enhancing E-Bike Efficiency with Intelligent Battery Temperature Control
by Tiago Gándara, Adriano Figueiredo, José Santos and Tiago Silva
World Electr. Veh. J. 2025, 16(6), 289; https://doi.org/10.3390/wevj16060289 - 22 May 2025
Abstract
This work presents an innovative approach to battery thermal management for e-bikes by addressing heat generation at its source rather than relying on conventional cooling techniques. Traditional systems rely on heat sinks, fans, phase change materials, or cooling fluids, which increase cost and [...] Read more.
This work presents an innovative approach to battery thermal management for e-bikes by addressing heat generation at its source rather than relying on conventional cooling techniques. Traditional systems rely on heat sinks, fans, phase change materials, or cooling fluids, which increase cost and complexity. In contrast, this study integrates embedded thermal management algorithms into the e-bike’s motor controller, enabling temperature regulation through performance limitation. Two models are investigated: a reactive algorithm that reduces speed as battery temperature nears a critical threshold, and a predictive algorithm that forecasts future temperature evolution and adjusts speed accordingly. Experimental results show that the reactive algorithm successfully limited battery temperature to 26.7% below the critical value but at the cost of speed reductions up to 40%. The predictive model, tested in two configurations, demonstrated improved performance, limiting speed by a maximum of 20% while maintaining stable temperature profiles. These findings confirm that embedded algorithms can effectively manage battery temperature, with the reactive model being suitable for low-complexity applications and the predictive model offering enhanced performance when more computational resources are available. Full article
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