Published: Sept. 12, 2024
Language: Английский
Published: Sept. 12, 2024
Language: Английский
Renewable and Sustainable Energy Reviews, Journal Year: 2024, Volume and Issue: 203, P. 114732 - 114732
Published: July 31, 2024
Language: Английский
Citations
23Energy, Journal Year: 2025, Volume and Issue: unknown, P. 134653 - 134653
Published: Jan. 1, 2025
Language: Английский
Citations
3Thermal Science and Engineering Progress, Journal Year: 2025, Volume and Issue: unknown, P. 103391 - 103391
Published: Feb. 1, 2025
Language: Английский
Citations
2Journal of Energy Storage, Journal Year: 2025, Volume and Issue: 111, P. 115438 - 115438
Published: Jan. 18, 2025
Language: Английский
Citations
1Energies, Journal Year: 2024, Volume and Issue: 17(19), P. 4932 - 4932
Published: Oct. 2, 2024
Accurate prediction of the Remaining Useful Life (RUL) lithium-ion batteries is essential for enhancing energy management and extending lifespan across various industries. However, raw capacity data these often noisy exhibits complex nonlinear degradation patterns, especially due to regeneration phenomena during operation, making precise RUL a significant challenge. Although deep learning-based methods have been proposed, their performance relies heavily on availability large datasets, satisfactory accuracy achievable only with extensive training samples. To overcome this limitation, we propose novel method that integrates sequence decomposition algorithms an optimized neural network. Specifically, Complementary Ensemble Empirical Mode Decomposition Adaptive Noise (CEEMDAN) algorithm employed decompose data, effectively mitigating noise from regeneration. Subsequently, Particle Swarm Optimization (PSO) used fine-tune hyperparameters Bidirectional Gated Recurrent Unit (BiGRU) model. The final BiGRU-based model was extensively tested eight battery datasets NASA CALCE, demonstrating robust generalization capability, even limited data. experimental results indicate CEEMDAN-PSO-BiGRU can reliably accurately predict batteries, providing promising reliable in practical applications.
Language: Английский
Citations
5Published: Jan. 1, 2025
In this study, the fire behavior of LiNi0.5Co0.3Mn0.2O2 (NCM) batteries was experimentally studied employing different thermal abuse methods. Two heating methods—namely, rod and disc—and three distinct continuous times (i.e., duration following a fire, referred to as CHT) are investigated. Fire characteristics such (including flame height width), mass loss, temperature distribution, smoke gas emissions were analyzed in detail. Results indicate that methods directly affect its combustion behavior. For given CHT, heated by disc exhibit more intense burning with higher jet fires due superior heat transfer efficiency uniform internal provided disc. Conversely, show pronounced exhaust phenomena persist for longer durations.As CHT increases, average disc-heated shows clear increasing trend. The total loss varies across CHTs reaches maximum at 60 s. contrast, battery rod, peaks = 30 s but decreases thereafter, accompanied decreasing trend loss. Finally, produces toxic gases after ignition, effects on concentration have been thoroughly examined. These findings provide critical insights into runaway lithium-ion significant importance enhancing their safety performance.
Language: Английский
Citations
0Journal of Energy Storage, Journal Year: 2025, Volume and Issue: 109, P. 115252 - 115252
Published: Jan. 5, 2025
Language: Английский
Citations
0Applied Thermal Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 125706 - 125706
Published: Jan. 1, 2025
Language: Английский
Citations
0SAE technical papers on CD-ROM/SAE technical paper series, Journal Year: 2025, Volume and Issue: 1
Published: Jan. 31, 2025
<div class="section abstract"><div class="htmlview paragraph">Thermal management system of electric vehicles (EVs) is critical for the vehicle's safety and stability. While maintaining components within their optimal temperature ranges, it also essential to reduce energy consumption thermal system. Firstly, a kind architecture integrated (ITMS) proposed, which can operate in multiple modes meet various demands. Two typical operating vehicle cooling summer heating winter, utilizes residual heat from drive system, are respectively introduced. The ITMS based on pump enables efficient transfer between different components. Subsequently, an model developed, including subsystems such as battery powertrain cabin description modeling process each subsystem provided detail. tested under world light test cycle (WLTC) condition six groups validate its feasibility. Next, dynamic objective control strategy proposed. It divides into time steps, where at step, non-dominated sorting genetic algorithm II (NSGA-II) employed perform multi-objective optimization temperature, state charge (SOC). Different objectives prioritized stages achieve control. A fusion developed by combining rule-based with Finally, comparative validation three strategies—rule-based control, control—is conducted 40°C conditions. results indicate that designed effective. not only achieves desirable but reduction certain extent, alleviates range anxiety.</div></div>
Language: Английский
Citations
0Applied Energy, Journal Year: 2024, Volume and Issue: 381, P. 125207 - 125207
Published: Dec. 26, 2024
Language: Английский
Citations
3