A Comprehensive Review of Multiple Physical and Data-Driven Model Fusion Methods for Accurate Lithium-Ion Battery Inner State Factor Estimation

Batteries, Journal Year: 2024, Volume and Issue: 10(12), P. 442 - 442

Published: Dec. 13, 2024

With the rapid global growth in demand for renewable energy, traditional energy structure is accelerating its transition to low-carbon, clean energy. Lithium-ion batteries, due their high density, long cycle life, and efficiency, have become a core technology driving this transformation. In lithium-ion battery storage systems, precise state estimation, such as of charge, health, power, crucial ensuring system safety, extending lifespan, improving efficiency. Although physics-based estimation techniques matured, challenges remain regarding accuracy robustness complex environments. advancement hardware computational capabilities, data-driven algorithms are increasingly applied management, multi-model fusion approaches emerged research hotspot. This paper reviews application between models critically analyzes advantages, limitations, applicability models, evaluates effectiveness robustness. Furthermore, discusses future directions improvement model adaptability, performance under operating conditions, aiming provide theoretical support practical guidance developing management technologies.

Language: Английский

User Behavior Analysis and Prediction Model Construction in Higher Education Management Information Systems

Applied Mathematics and Nonlinear Sciences, Journal Year: 2024, Volume and Issue: 9(1)

Published: Jan. 1, 2024

Abstract The education management system is an important tool for universities to manage academic information and of staff students. article constructs a user behavior analysis model based on machine learning prediction LR-XGBoost analyze predict in the educational colleges universities. Specifically, after collection data university system, descriptive statistics cluster are performed. Then, relationship between dropout/leaving explored by model, performance tested comparing accuracy with other models. Cluster 3 users had significantly higher means 12 behaviors college than those clusters 2 1. Among behaviors, only closing web pages p-value greater 0.05 did not pass z-test. best 95.51% 94.53% behavioral anomalous prediction, respectively, F1 values 92.34% 95.33%, respectively. It has clear overall advantage predicting next 1-10 days.

Language: Английский

Pattern Identification of Community Engagement Behaviors in a Big Data Environment and Its Impact on Community Health Development

Applied Mathematics and Nonlinear Sciences, Journal Year: 2024, Volume and Issue: 9(1)

Published: Jan. 1, 2024

Abstract Improving residents’ community participation capacity in the big data environment helps to realize common action and resource sharing among subjects promotes healthy development of community. This paper takes theory planned behavior ladder civic as guide, designs distributes questionnaires related patterns obtain research data, identifies using K-Means clustering algorithm introduces PROMETHEE Ⅱ method measure patterns. The impact model health based on structural equations was constructed analyze degree development. There are six categories behavioral patterns, overall net flow complete is highest at 0.047. coefficient equation 0.149, test result has a significant 1% level. Different behaviors can promote help improve governance.

Language: Английский