人工智能與基于模型設(shè)計(jì)在電池容量評(píng)估中的應(yīng)用

0人工智能與基于模型設(shè)計(jì)在電池容量評(píng)估中的應(yīng)用馬文輝,MathWorks主要內(nèi)容基于AI的SOC估計(jì)方法將SOC估計(jì)的AI模型引入到基于模型設(shè)計(jì)（MBD）MATLAB與Simulink聯(lián)合仿真測(cè)試SOC1VoltageCurrentTemperature電池荷電狀態(tài)(State

of

Charge,SOC)電池荷電狀態(tài)（State

OfCharge，SOC）是一個(gè)表征電池系統(tǒng)可靠性的重要屬性。對(duì)于單體電池，SOC可以表示為可用荷電量與額定荷電量之間的比率：??????????

=????其中，????表示當(dāng)前可用電荷量，????表示額定電荷量。SOC2SOC估計(jì)SOC

估計(jì)作為電池管理系統(tǒng)（BMS）的核心功能之一。準(zhǔn)確的SOC

估計(jì)結(jié)果能夠在電池壽命、安全可靠性、利用率等方面對(duì)電源管理系統(tǒng)提供有效的支持。電池的SOC無(wú)法直接測(cè)量采用間接方法測(cè)量SOC擴(kuò)展的卡爾曼濾波數(shù)據(jù)驅(qū)動(dòng)的方法3Battery

State

of

Charge

(SOC)SOC估計(jì)-擴(kuò)展的卡爾曼濾波需要構(gòu)建詳細(xì)的電池模型需要較大的計(jì)算量面對(duì)復(fù)雜系統(tǒng)難以建模準(zhǔn)確性較低currentvoltage

measurement+-KalmanFilterbatterymodelvoltageestimationSOC4SOC估計(jì)–數(shù)據(jù)驅(qū)動(dòng)的方法基于歷史運(yùn)行數(shù)據(jù)訓(xùn)練AI模型（機(jī)器學(xué)習(xí)或深度學(xué)習(xí)模型）不需要考慮電池建模AI模型的離線訓(xùn)練，在線預(yù)測(cè)，響應(yīng)速度快可以應(yīng)該各種復(fù)雜系統(tǒng)需要獲取大量的電池運(yùn)行數(shù)據(jù)黑盒模型，缺乏可解釋性Battery

State

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