Li-Ion Pil Termal Parametre Tanımlama ve Çekirdek Sıcaklık Tahmini

Öz

Pil çekirdeği ve yüzey sıcaklığı, Li-ion pillerin termal yönetimi ve güvenli kullanımı için çok önemlidir. Onlar, hücrenin fiziksel özelliklerini etkilerler ve pil şarj durumu (SOC) ve sağlık durumu (SOH) gibi bazı temel durumları ile güçlü bir korelasyona sahiptirler. Bu nedenle, pil çekirdeğinin ve yüzey sıcaklığının doğru bir tahmini, performansı artıracak ve pilin ömrünü uzatacaktır. Bu çalışma, pil çekirdeği ve yüzey sıcaklığı için bir tahmin sistemi önerilmektedir. Pil SOC'sini, çekirdek ve yüzey sıcaklığını yakalamak için basitleştirilmiş bir sözde iki boyutlu model tanıtılmış, sonrasında bu çalışmada elde edilen sonuçları doğrulamak ve modellemek için kullanılmıştır. Ardından, iki durumlu bir termal pil modeli (TSM) sunulmuş ve incelenmiştir. Pilin termal parametrelerini tanımlamak için özyinelemeli en küçük kareler (RLS) algoritması benimsenmiştir. Daha sonra, TSM; COMSOL Multiphysics simülasyon yazılımı kullanılarak doğrulanmış ve sonrasında termal parametreler, pil çekirdek sıcaklığını tahmin etmek için Kalman filtresine (KF) uygulanmıştır. Sonuç olarak, pil çekirdek sıcaklığı tahmini sonuçlarının doğruluğu 0.037K'lık bir ortalama karekök hatasıyla doğrulanmıştır.

Anahtar Kelimeler

• Li-ion pil termal modeli
• çekirdek sıcaklığı
• yüzey sıcaklığı
• Kalman filtresi
• özyinelemeli en küçük kareler

Lithium-Ion Battery Thermal Parameter Identification and Core Temperature Estimation

Öz

Battery core and surface temperature are crucial for the thermal management and safety usage of Li-ion batteries. They affect the cell's physical properties and strongly correlate with some of its key states, such as the battery state of charge (SOC) and state of health (SOH). Therefore, an accurate estimate of the battery core and surface temperature will enhance the performance and prolong the battery's life. This study proposes an estimation system of the battery core and surface temperature. A simplified pseudo-two-dimensional model is introduced to capture the battery SOC, core and surface temperature that will be used later in this study to model and validate the results' accuracy. Then, a two-state thermal battery model (TSM) is presented and studied. The recursive least square (RLS) algorithm is adopted to identify the thermal parameters of the battery. Next, the TSM is validated using COMSOL Multiphysics simulation software and the thermal parameters are then fed to the Kalman filter (KF) to estimate the battery core temperature. Finally, the accuracy of the battery core temperature estimated results are validated with a root mean square error of 0.037K.

Anahtar Kelimeler

• Li-ion battery thermal model
• core temperature
• surface temperature
• Kalman filter
• RLS

Kaynakça

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