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Electric vehicle battery parameter identification and SOC observability analysis: NiMH and Li-S case studies

機(jī)譯：電動(dòng)汽車電池參數(shù)識(shí)別和SOC可觀察性分析：NiMH和Li-S案例研究

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In this study, a framework is proposed for battery model identification to be applied in electric vehicle energy storage systems. The main advantage of the proposed approach is having capability to handle different battery chemistries. Two case studies are investigated: nickel-metal hydride (NiMH), which is a mature battery technology, and Lithium-Sulphur (Li-S), a promising next-generation technology. Equivalent circuit battery model parametrisation is performed in both cases using the Prediction-Error Minimization (PEM) algorithm applied to experimental data. The use of identified parameters for battery state-of-charge (SOC) estimation is then discussed. It is demonstrated that the set of parameters needed can change with a different battery chemistry. In the case of NiMH, the battery’s open circuit voltage (OCV) is adequate for SOC estimation. However, Li-S battery SOC estimation can be challenging due to the chemistry’s unique features and the SOC cannot be estimated from the OCV-SOC curve alone because of its flat gradient. An observability analysis demonstrates that Li-S battery SOC is not observable using the common state-space representations in the literature. Finally, the problem’s solution is discussed using the proposed framework.
機(jī)譯：在這項(xiàng)研究中，提出了一種用于電池模型識(shí)別的框架，該框架將應(yīng)用于電動(dòng)汽車儲(chǔ)能系統(tǒng)。提出的方法的主要優(yōu)點(diǎn)是具有處理不同電池化學(xué)成分的能力。研究了兩個(gè)案例研究：成熟的電池技術(shù)鎳氫（NiMH）和有前途的下一代技術(shù)鋰硫（Li-S）。在兩種情況下，均使用應(yīng)用于實(shí)驗(yàn)數(shù)據(jù)的預(yù)測(cè)誤差最小化（PEM）算法執(zhí)行等效電路電池模型參數(shù)化。然后討論將識(shí)別出的參數(shù)用于電池充電狀態(tài)（SOC）估計(jì)。結(jié)果表明，所需的參數(shù)集會(huì)隨著電池化學(xué)性質(zhì)的不同而變化。對(duì)于NiMH，電池的開(kāi)路電壓（OCV）足以進(jìn)行SOC估算。但是，由于化學(xué)性質(zhì)的獨(dú)特性，Li-S電池SOC的估算可能具有挑戰(zhàn)性，并且由于其平坦的梯度，無(wú)法僅從OCV-SOC曲線估算SOC?？捎^察性分析表明，使用文獻(xiàn)中常見(jiàn)的狀態(tài)空間表示法無(wú)法觀察到Li-S電池SOC。最后，使用建議的框架討論了問(wèn)題的解決方案。

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