Temperature prediction of lithium batteries based on weighted UMAP and improved BLS

doi:10.19799/j.cnki.2095-4239.2024.0549

Abstract

Abstract:

Predicting the temperature of the thermal process in lithium batteries has significant implications for the lifespan management and safety of these batteries. Thermal management in battery management systems typically depends on accurate thermal process models. However, the thermal process in lithium batteries is complex, constituting a strongly nonlinear distributed parameter system with the characteristics of parameter spatiotemporal coupling, time variation, and strong nonlinearity. Conventional methods struggle to accurately model this thermal process.To address these issues, this study proposes a three-stage lithium battery thermal process modeling method based on weighted uniform manifold approximation and projection (WUMAP) and an improved broad learning system (BLS). First, a WUMAP dimensionality reduction algorithm was introduced to solve the nonlinear dimensionality reduction problem while preserving global and local data information. Then, a BLS model was introduced to predict the temporal data obtained from the dimensionality reduction. Finally, a mixed multikernel BLS (MKBLS) model optimized by particle swarm optimization was used to reconstruct the spatiotemporal temperature data.To validate the effectiveness of the model, modeling experiments were conducted on the thermal process of a flat plate 32 Ah Li(Ni_0.5Co_0.2Mn_0.3)O₂ ternary lithium battery. The experimental results show that the final model, compared to its previous version, increased R² by 0.0546 and decreased MAE and RMSE by 0.0082 and 0.0092, respectively. When compared with several other models, the final model demonstrated a lower relative error (ARE) of less than 0.035 and better performance on all error indicators, confirming its high prediction accuracy.

Key words: distributed parameter system, temperature prediction, weighted uniform manifold approximation and projection (WUMAP), mixed kernel broad learning system (MKBLS)

CLC Number:

TM 912.9

Yaokang LI, Haidong YANG, Kangkang XU, Zhaoyu LAN, Runnan ZHANG. Temperature prediction of lithium batteries based on weighted UMAP and improved BLS[J]. Energy Storage Science and Technology, 2024, 13(9): 3006-3015.

Figures/Tables 10

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预测模型	R²	MAE	RMSE	MSE
WUMAP-BLS-MKBLS	0.9731	0.0081	0.0129	0.000166
WUMAP-BLS-K_RBFBLS	0.9705	0.0085	0.0134	0.000180
WUMAP-BLS-K_ployBLS	0.9632	0.0097	0.0149	0.000222
LLE-ELM	0.9619	0.0104	0.0144	0.000210
KL-ELM	0.9396	0.0148	0.0187	0.000350
WUMAP-BLS-BLS	0.9355	0.0142	0.0193	0.000372
UMAP-BLS-BLS	0.9185	0.0163	0.0221	0.000488
t-SNE-BLS-BLS	0.8640	0.0226	0.0295	0.000870

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