0人工智能與基于模型設計在電池容量評估中的應用馬文輝,MathWorks主要內容基于AI的SOC估計方法將SOC估計的AI模型引入到基于模型設計（MBD）MATLAB與Simulink聯合仿真測試SOC1VoltageCurrentTemperature電池荷電狀態(State

of

Charge,SOC)電池荷電狀態（State

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

=????其中，????表示當前可用電荷量，????表示額定電荷量。SOC2SOC估計SOC

估計作為電池管理系統（BMS）的核心功能之一。準確的SOC

估計結果能夠在電池壽命、安全可靠性、利用率等方面對電源管理系統提供有效的支持。電池的SOC無法直接測量采用間接方法測量SOC擴展的卡爾曼濾波數據驅動的方法3Battery

State

of

Charge

(SOC)SOC估計-擴展的卡爾曼濾波需要構建詳細的電池模型需要較大的計算量面對復雜系統難以建模準確性較低currentvoltage

measurement+-KalmanFilterbatterymodelvoltageestimationSOC4SOC估計–數據驅動的方法基于歷史運行數據訓練AI模型（機器學習或深度學習模型）不需要考慮電池建模AI模型的離線訓練，在線預測，響應速度快可以應該各種復雜系統需要獲取大量的電池運行數據黑盒模型，缺乏可解釋性Battery

State

of

Charge

(SOC)5AI與基于模型設計(MBD)集成開發的流程Data

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desktop數據預處理仿真測試6集成部署AI建模利用MATLAB和Simulink，可以將AI算法應用于模型設計中數據預處理數據獲取運行數據，通過傳感器采集實驗室數據仿真數據，故障仿真，衰退仿真特征提取、衍生時域特征頻域特征Data

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desktop數據預處理仿真測試集成部署AI建模8Data

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模型構建、訓練與驗證MATLAB支持通過圖形化工具設計、訓練、驗證機器學習模型和深度神經網絡模型深度神經網絡模型訓練深度神經網絡設計9模型超參數設定機器學習模型訓練，對比和驗證機器學習可解釋性機器學習模型可解釋性Local

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desktop數據預處理仿真測試集成部署AI建模TFimporter11導入第三方平臺訓練好的深度神經網絡模型MATLAB支持直接導入在第三方平臺訓練好的深度神經網絡模型，如TensorFlow、Caffe；也支持導入ONNX（Open

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Exchange）格式的模型文件Simulink

仿真測試Simulink模塊庫包含機器學習和深度學習（CNN和LSTM）模塊，通過直接拖入的方式引入這些模塊并通過配置模型文件關聯

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desktop數據預處理仿真測試集成部署AI建模1213模型的部署Data

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desktop數據預處理仿真測試集成部署AI建模NXP

S32K344Automatic

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CodeAnyCPUInc.

ARM

Cortex-M141案例：利用NXP目標板構建PIL仿真5總結MATLAB與Simulink構建AI與MBD集成開發的流程MATLAB與Simulink相結合實現基于AI的電池SOC的仿真，驗證與部署。SOC16VoltageCurrentTemperature17謝謝?

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