Integrated Mixed Attention U-Net Mechanisms with Multi-Stage Division Strategy Customized for Accurate Estimation of Lithium-Ion Battery State of Health
Abstract
:1. Introduction
- An MMAU-Net method using a multi-stage strategy and a mixed-attention mechanism is proposed to accurately estimate battery SOH, which is more effective in extracting the aging information of batteries at multiple stages.
- A multi-stage division method based on geometric knee point segmentation is proposed to divide the battery’s multi-level degradation feature stages to enable the network to extract and recognize features more efficiently.
- A mixed attention U-Net model for degradation feature extraction is proposed. Using a one-dimensional (1D) operating module to accommodate battery degradation data inputs. The incorporation of a hybrid attention module into U-Net reduces the redundant information generated during feature extraction and improves the ability to focus on multi-level features.
2. Multi-Stage Division Method for Battery Degradation Performance Estimation
2.1. Overview of Battery Degradation Cycle
2.2. Battery Multi-Stage Characteristics
2.3. Multi-Stage Geometric Knee Point Division Method
3. The Proposed Encoder–Decoder Prediction Model
3.1. Review and Analysis of the Existing U-Net Model
3.2. Mixed Attention U-Net Network Architecture
3.2.1. Improved U-Net for SOH Estimation
3.2.2. Mixed Attention Module
4. Experimental Results
4.1. Datasets
4.2. Experiment Setting and Evaluation Metrics
4.3. Experimental Results and Analysis
4.3.1. Performance Evaluation of Multi-Stage Partitioning Strategy
4.3.2. Performance Evaluation of the Proposed SOH Estimation Method
5. Conclusions
- (1)
- Considering the lack of specific stage division methods that incorporate multi-stage degradation performance of batteries, this paper transforms the multi-stage division task into the use of geometric division of multiple knee points by incorporating the practical significance of the knee point in battery degradation. The method is not only capable of categorizing battery degradation data based on degradation trends but is also simple to apply and interpretive.
- (2)
- In order to reduce the feature redundant information in the feature extraction process and improve the depth of degraded information extraction, this paper proposes an enhanced U-Net model with a hybrid attention mechanism. The mixed attention U-Net performs deep extraction of aging features through the encoder–decoder structure of U-Nets and reduces redundant feature information by incorporating hybrid attention. The mixed attention U-Net model, evaluated in Bach3 as MAE 0.86 and RMSE 1.08, performs well in the other batches, and also outperforms the LSTM, GRU, and MLP models in the remaining batches. The experimental results demonstrate the feasibility and validity of the proposed model.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Battery ID | Multi-Stage (Cycle) | ||
---|---|---|---|
1 | 1~28 | 29~236 | 237~389 |
2 | 1~26 | 27~229 | 230~406 |
3 | 1~26 | 27~221 | 222~392 |
4 | 1~27 | 28~230 | 231~395 |
5 | 1~24 | 25~234 | 235~402 |
6 | 1~28 | 29~245 | 246~407 |
7 | 1~19 | 20~233 | 234~401 |
8 | 1~28 | 29~278 | 279~419 |
LSTM | ||||
---|---|---|---|---|
Multi-Stage | Non-Stage | |||
MAE (%) | RMSE (%) | MAE (%) | RMSE (%) | |
Batch1 | 1.93 | 2.68 | 2.01 | 2.79 |
Batch2 | 1.49 | 1.82 | 1.55 | 1.92 |
Batch3 | 1.75 | 2.55 | 2.48 | 3.25 |
U-Net | ||||
---|---|---|---|---|
Multi-Stage | Non-Stage | |||
MAE (%) | RMSE (%) | MAE (%) | RMSE (%) | |
Batch1 | 1.14 | 1.65 | 1.46 | 1.67 |
Batch2 | 1.27 | 1.85 | 1.28 | 1.62 |
Batch3 | 0.81 | 1.21 | 1.48 | 1.71 |
Mixed Attention U-Net | ||||
---|---|---|---|---|
Multi-Stage | Non-Stage | |||
MAE (%) | RMSE (%) | MAE (%) | RMSE (%) | |
Batch1 | 1.02 | 1.40 | 1.28 | 1.5 |
Batch2 | 1.04 | 1.50 | 1.16 | 1.42 |
Batch3 | 0.66 | 1.04 | 0.86 | 1.08 |
Type | Method | Source Battery | RMSE (%) | MAE (%) |
---|---|---|---|---|
Non-stage | LSTM | Batch 1 | 2.79 | 2.01 |
Batch 2 | 1.92 | 1.55 | ||
Batch 3 | 3.25 | 2.48 | ||
GRU | Batch 1 | 2.78 | 2.13 | |
Batch 2 | 2.19 | 1.79 | ||
Batch 3 | 3.61 | 2.89 | ||
U-Net | Batch 1 | 1.67 | 1.46 | |
Batch 2 | 1.62 | 1.28 | ||
Batch 3 | 1.71 | 1.48 | ||
MLP | Batch 1 | 1.76 | 1.49 | |
Batch 2 | 1.58 | 1.24 | ||
Batch 3 | 1.52 | 1.32 | ||
Mixed Attention U-Net | Batch 1 | 1.50 | 1.28 | |
Batch 2 | 1.42 | 1.16 | ||
Batch 3 | 1.08 | 0.86 | ||
Multi-stage | Mixed Attention U-Net | Batch 1 | 1.40 | 1.02 |
Batch 2 | 1.50 | 1.04 | ||
Batch 3 | 1.04 | 0.66 |
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Fan, X.; Yang, X.; Hou, F. Integrated Mixed Attention U-Net Mechanisms with Multi-Stage Division Strategy Customized for Accurate Estimation of Lithium-Ion Battery State of Health. Electronics 2024, 13, 3244. https://doi.org/10.3390/electronics13163244
Fan X, Yang X, Hou F. Integrated Mixed Attention U-Net Mechanisms with Multi-Stage Division Strategy Customized for Accurate Estimation of Lithium-Ion Battery State of Health. Electronics. 2024; 13(16):3244. https://doi.org/10.3390/electronics13163244
Chicago/Turabian StyleFan, Xinyu, Xuxu Yang, and Feifei Hou. 2024. "Integrated Mixed Attention U-Net Mechanisms with Multi-Stage Division Strategy Customized for Accurate Estimation of Lithium-Ion Battery State of Health" Electronics 13, no. 16: 3244. https://doi.org/10.3390/electronics13163244