An Enhanced Cascaded Deep Learning Framework for Multi-Cell Voltage Forecasting and State of Charge Estimation in Electric Vehicle Batteries Using LSTM Networks

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dc.contributor.authorPourbunthidkul, Supavee
dc.contributor.authorPahaisuk, Narawit
dc.contributor.authorLaon, Popphon
dc.contributor.authorHoungkamhang, Nongluck
dc.contributor.authorPhasukkit, Pattarapong
dc.date.accessioned2026-08-06T10:51:22Z
dc.date.available2026-08-06T10:51:22Z
dc.date.issued2025-06-01
dc.description.abstractEnhanced Battery Management Systems (BMS) are essential for improving operational efficacy and safety within Electric Vehicles (EVs), especially in tropical climates where traditional systems encounter considerable performance constraints. This research introduces a novel two-tiered deep learning framework that utilizes a two-stage Long Short-Term Memory (LSTM) framework for precise prediction of battery voltage and SoC. The first tier employs LSTM-1 forecasts individual cell voltages across a full-scale 120-cell Lithium Iron Phosphate (LFP) battery pack using multivariate time-series data, including voltage history, vehicle speed, current, temperature, and load metrics, derived from dynamometer testing. Experiments simulate real-world urban driving, with speeds from 6 km/h to 40 km/h and load variations of 0, 10, and 20%. The second tier uses LSTM-2 for SoC estimation, designed to handle temperature-dependent voltage fluctuations in high-temperature environments. This cascade design allows the system to capture complex temporal and inter-cell dependencies, making it especially effective under high-temperature and variable-load environments. Empirical validation demonstrates a 15% improvement in SoC estimation accuracy over traditional methods under real-world driving conditions. This study marks the first deep learning-based BMS optimization validated in tropical climates, setting a new benchmark for EV battery management in similar regions. The framework’s performance enhances EV reliability, supporting the growing electric mobility sector.
dc.identifier.citationSensors, 25(12), 2025
dc.identifier.doi10.3390/s25123788
dc.identifier.issn14248220
dc.identifier.other2-s2.0-105009062693
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/17028
dc.sourceSensors
dc.subjectbattery management system
dc.subjectdeep learning
dc.subjectelectric vehicles
dc.subjectlithium iron phosphate battery
dc.subjectlong short-term memory model
dc.subjectstate of charge
dc.titleAn Enhanced Cascaded Deep Learning Framework for Multi-Cell Voltage Forecasting and State of Charge Estimation in Electric Vehicle Batteries Using LSTM Networks
dc.typeArticle

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