具線上循環壽命估測與延壽機能之回收大型電動車鋰離子電池管理系統開發

Development of Battery Management System Embedded with Online Life-cycle Estimation and Elongation for Reusing Large-scale Automotive Lithium-ion Battery

袁文璞(Wen-Poo Yuan)

指導教授 ： 施武陽 ； 江益賢

中原大學/工學院/環境工程學系/碩士(2019年)

https://doi.org/10.6840/cycu201900598

摘要

在台灣，隨著電動車快速的發展，電動汽車的銷售量每年約超過一萬輛，然而電動車的動力電池汰換快速，這些汰換電池的回收一直以來都是非常大的問題，即使電池面臨汰換，仍具70%-80%的存儲能量可供使用，而這些汰役電池易於集成且成本僅為原電池的1/3至1/4，因此若作為儲能設備使用，對於再生能源的進展是一大助力，儲能系統的建置能夠使風能及太陽能等不穩定的能源，更易於與電網或家戶用電整合，有利於提高分散式能源系統的穩定性，消費者也可以選擇在有利的時間儲電與用電，然而這些再生電池的老化速度比原電池快，同時不安全性也較高，因此需要額外添加電池管理系統來延長生命週期，加強系統運作的可靠性，並減緩電池容量的衰減。
本研究建立一個鋰離子電池在線參數估測的電池管理系統，此系統結合電流感測器ACS759、電池組監控晶片ISL78600ANZ、資料傳輸收發器I-7565-H1、及開發線上自適應控制法進行電池循環壽命的估測，並利用MATLAB/Simulink進行數學模型的建立，基於Lyapunov穩定性標準，可以非常正確地估計電池的開路電壓與內電阻，誤差值小於1％，表明該估測算法技術的適用性及有效性。同時本系統可實現自動進行數據的採集和處理，通過基本的CAN(控制器局部網路)與SPI(串型外設接口)實時獲得電池狀態，如電流、電壓和溫度，帶入估測算法後進一步計算電池的SoC(荷電狀態)和SoH(健康狀態)。將整套系統與超級電容並聯，利用脈衝調控對電流間的分配比例進行調控，進而延長電池的使用壽命。
我們的目標是在污水處理廠使用整個管理系統來存儲不穩定的可再生能源，在離尖峰用電的使用情況下，達到最佳的能源使用效率。無論是在經濟方面還是在環境方面，這項技術必將具有更廣闊的發展前景。

關鍵字

鋰離子電池 ； 自適應性控制算法 ； 開路電壓 ； 內電阻 ； SOC(荷電狀態)

並列摘要

Due to increasing concerns about greenhouse gas emissions, and the depletion of fossil fuels, the electric vehicles (EVs) receive massive popularity in recent decades. Lithium-ion batteries have attained huge attention in EVs application due to their lucrative features. In Taiwan, the number of EVs’ sales are more than 10,000 units per year and increasing year by year. It becomes an important issue that how to recycle used batteries for these electric vehicles. The renewable sources usually required storage system due to change in the power outputs during the day. Recycled lithium-ion battery (RLIB) pack refers to the methods used to store electricity on a large scale within an electrical power grid. This is done to increase efficiency and lower the cost of energy production, or to facilitate the use of intermittent energy sources. However an increase in demand for using batteries, the charging process of battery system needed to be well managed through an adaptive controlled energy managing system.
In this study, a battery management system for battery electric vehicles hybridized with the supercapacitor is proposed. We have already developed the new adaptive algorithm based on Lyapunov which can very correct to estimate the important parameters of the battery, and the error value is smaller than 1%. The real-time simulator which can achieve automatic data acquisition and processing, further to estimate the battery's SOC (State of Charge) and SOH (State of Health). We can effective in extending the battery's life and estimate how many times of charge/discharge that the RLIB can afford. We aim using this entire management system in wastewater treatment plant for storing renewable energies. Whether in economic or environmental terms This technique is bound to have an even broader prospect for development.

並列關鍵字

Adaptive control ； Battery Management System(BMS) ； SOC ； OCV

參考文獻

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