使用反應曲面以提升電池電量在動態運行下的預測精度

Abstract

本研究旨在發展可應用於電動車電池電量(State of Charge, SOC)估測的演算法。演算方法必須滿足無需高運算能力硬體，考量電池SOC影響因子以及可應用於電動車動態的行車行為中。為此，本研究發展了一套結合開路電壓查表法(OCV method)與庫倫積分法(Coulomb counting method)的優化方法用以估測鋰電池SOC。此優化方法藉由實驗數據建立反應曲面，加入溫度、電池電流等電池SOC估測影響因子以改善並提升電池SOC估測精準度。除此，本研究提出了一套可用於電動車動態充電以及放電環境的電池SOC估測方法，以改善非靜置狀態無法使用開路電壓查表法的問題。更全面的電池SOC估測，不僅可在靜置狀態修正電池SOC，也可在充放電池狀態進行準確電池SOC估測，致使電池在整體使用過程中擁有更可靠的電池SOC。實驗結果顯示優化方法的預測準確性高於原有的開路電壓查表法與庫倫積分法的應用。

The goal of the thesis is to come up with an algorithm that is adequate for real-time electric vehicle battery state of charge(SOC) estimation. Therefore, the algorithm should meet the requirement of not hardware performance demanding, considering factors that influence battery SOC estimation and most importantly able to perform in dynamic operating state of electric vehicle. To cope with this, the study developed an improved algorithm based on combination of open-circuit voltage (OCV) method and coulomb counting method to estimate the SOC of lithium-ion battery. The proposed algorithm builds several surrogate models based on experimental data, and considers various influential issues such as temperature influence, battery current to improve SOC estimation. In addition, a methodology to estimate battery initial SOC during more realistic charging and discharging dynamic environment is proposed to cope with the unavailability of OCV method when not in rest state. In other words, the estimation of battery SOC is more comprehensive and can be corrected more often resulting in a more reliable battery SOC throughout battery usage. Experimental results of the algorithm shown better accuracy compared to basic OCV-Coulomb counting method.