Intelligent Optimization of Battery Management System Parameters for Improved Performance of LiFePO₄ Batteries in Electric Vehicles
電気自動車用LiFePO₄バッテリーの性能向上のためのバッテリーマネジメントシステムパラメータの知的最適化 (AI 翻訳)
Neelima Dudhe, Z J.Khan
🤖 gxceed AI 要約
日本語
本論文は、電気自動車（EV）用LiFePO4バッテリーのバッテリーマネジメントシステム（BMS）の設計パラメータ最適化を扱う。特に充電状態（SOC）推定に焦点を当て、様々な動作条件や劣化シナリオ下での性能を検証。電圧バランス、熱戦略、電流制限、健全状態（SOH）指標などの主要パラメータを評価し、モデリングと実試験で有効性を確認した。
English
This paper presents comprehensive optimization of Battery Management System (BMS) parameters for LiFePO4 batteries in electric vehicles, focusing on State of Charge (SOC) estimation. Key parameters include voltage balance, thermal management, current limits, and state-of-health indicators. Validation through modeling and real-time testing demonstrates improved performance and reliability.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本のEV市場拡大とバッテリー技術開発において、LiFePO4バッテリーのBMS最適化は航続距離向上や安全性確保に直結する。本研究成果は日本の自動車メーカーやバッテリーサプライヤーにとって実用的な指針となる。
In the global GX context
As global EV adoption accelerates, optimized BMS for LiFePO4 batteries—widely used in entry-level EVs and stationary storage—enhances safety and efficiency. This paper offers practical insights for BMS developers and contributes to the broader energy transition.
👥 読者別の含意
🔬研究者:This paper provides validated design criteria and SOC estimation methods for LiFePO4 BMS, useful for battery management research.
🏢実務担当者:Engineers can apply the parameter optimization framework to improve EV battery performance and longevity.
📄 Abstract（原文）
Abstract The growing demand for efficient, reliable, and safe electric vehicles (EVs) has elevated the significance of advanced BMS, particularly for LFP (LiFePO₄) Battery Technologies. This paper presents a comprehensive examination of major design criteria that influence the efficiencies, reliability, and longevity of Battery Management Systems (BMS) intended for electric vehicle (EV) use. Emphasis is placed on accurately estimating the State of Charge (SOC), an essential component of proper battery health monitoring and management of energy used.Various SOC calculation methods are analyzed, The study explores how these methods respond under different operational conditions and battery aging scenarios. Additionally, Key parameters including voltage balance, thermal strategies, current limits, and state-of-health (SOH) indicators have been assessed for their contributions towards system performance. The effectiveness of the proposed SOC estimation and parameter optimization has been validated through modeling and real-time testing. The insights gained from this work will aid researchers in the development of a durable and adaptable BMS design tailor-made for LiFePO₄ batteries used in today's electric vehicles. A potential divider-based current sensor monitors the load dynamics, while a temperature-stable OP-AMP filter design refines signal integrity. Experimental results validate system efficiency through 12 time- series plots (voltage, current, and power across PV modules), extracted from MATLAB and Excel-based post-processing. The solution is modular, programmable, cost-effective, and highly adaptable for smart-grid and off-grid solar applications. Keywords Solar Tracking System, MPPT, LiFePO4 Bat- tery, Data Acquisition System (DAS), Stepper Motor Control, PIC Microcontroller, Renewable Energy, Embedded Systems, PV Efficiency, Real-Time Monitoring.
🔗 Provenance — このレコードを発見したソース
- Zenodo https://zenodo.org/records/21256607first seen 2026-07-09 04:16:49
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