HyLife-FC Platform

A Scalable Weighted Multi-Region Machine Learning Platform for Generalizable Fuel Cell Lifetime Prediction in Heavy-Duty Trucks

Platform Overview

HyLife-FC is an online decision-making and visualization platform based on the paper "HyLife-FC: A Scalable Weighted Multi-Region Machine Learning Platform for Generalizable Fuel Cell Lifetime Prediction in Heavy-Duty Trucks".

The platform integrates Savitzky–Golay denoising, t-SNE visualization with SVM/GMM combination classification, current density-based LSTM predictors, and similarity-weighted XGBoost fusion to achieve high-precision prediction of fuel cell stack voltage degradation in heterogeneous operating environments with uncertainty intervals.

Users can upload vehicle operation data to intuitively view t-SNE similarity distribution, model prediction curves, and 98% confidence intervals. The platform supports batch evaluation and maintenance recommendations, helping manufacturing and maintenance units reduce testing costs and optimize maintenance plans.

High Precision Prediction
Accurate fuel cell stack voltage degradation forecasting with uncertainty intervals
Data Analysis
Advanced algorithms for fuel cell performance analysis
Advanced Algorithms
Combination of Savitzky-Golay, t-SNE, SVM/GMM, LSTM and XGBoost

Platform Features Showcase

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中文介绍视频

HyLife-FC平台功能演示与操作指南

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English Introduction

Similarity Analysis Based on t-SNE Dimensionality Reduction and SVM/GMM Combined Classification

System Architecture Diagram

Data Source
Data Upload
User-uploaded CSV/Excel files containing fuel cell operation data
Preprocessing & Feature Storage
Data Denoising
Savitzky-Golay filtering techniques
Feature Extraction
Identify important data features
Feature Storage
Local storage for processed features
Dimensionality Reduction & Similarity
Data Embedding
t-SNE and UMAP algorithms
Similarity Weights
SVM and GMM model calculations
Pattern Database
Store pattern relationships
Model Inference Layer
LSTM Models
Region/current-specific forecasting with deep learning capabilities for time-series analysis
XGBoost Fusion
Similarity-weighted model ensemble for robust predictions across diverse operating conditions
Quantile Regression
Advanced statistical methods for calculating 98% confidence intervals and uncertainty quantification
Model Serving
High-performance model serving with auto-scaling capabilities
Application Frontend
API & Processing
Backend services for data processing and analysis
Dashboard
Interactive dashboard built with HTML, CSS and JavaScript
Ready to Launch
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