State of charge estimation for lithium-based batteries

Name: State of charge estimation for lithium-based batteries
Author: Çelik, Mert

İsim	State of charge estimation for lithium-based batteries
Yazar	Çelik, Mert
Basım Tarihi:	2020-01
Konu	SOC estimation, Extended kalman filter, Convergence time, Computational time
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Kayıt Numarası	66e616e9-4714-4bf4-a3d5-70f844768179
Lokasyon	Department of Electrical and Electronics Engineering
Tarih	2020-01
Örnek Metin	This thesis proposes a new State of Charge (SOC) estimation method for lithiumbased batteries, which o ers a good trade-o between convergence and computation times. Lithium-based battery packages are quite common in the automotive industry and beyond because of their high-power density and dynamic response capabilities. Per a given volume, lithium-based battery cells have much more capacity, higher Crates, and lower internal resistance than other cell chemistries. However, this comes at a cost because of lithium's reactive nature. It is hard to preserve, monitor, cool, and control lithium in a pack within a safe state. For these reasons, battery control, or in other words, Battery Management Systems (BMS) is a major topic in the literature, and estimation of SOC, State of Health (SOH), and State of Power (SOP) are considered as core subfunctions of BMS. This thesis focuses on improving SOC estimation for lithium-based batteries. SOC estimation determines the remaining charge level on the battery and is very critical for battery-powered devices. This process is relatively straightforward when the battery is in the resting state. However, it can be di cult while the battery-powered device is operating, due to process disturbances and model uncertainties. The best performing SOC estimation methods in the literature are based on Kalman Filtering, and they are specifically Extended Kalman Filter (EKF) and Adaptive Dual Extended Kalman Filter (ADEKF). While EKF offers the shortest computation time, it results in a long convergence time. On the other hand, ADEKF o ers short convergence time and long computation time. We propose PID-controlled EKF, which o ers a mid-point in terms of convergence and computation times. The importance of convergence characteristics are also articulated in this thesis, especially from an automotive perspective., Bu tez Lityum tabanl piller i cin yak nsama performans ve hesaplama karma s kl g dengelenmi s yeni bir SOC kestirim algoritmas sunmaktad r. Lityum bazl piller yuksek enerji yo gunlu guna ve dinamik tepkilere sahip oldu gu i cin endustride olduk ca yayg n olarak tercih edilmektedir. Bu piller, belirli bir hacimde di ger hucre kimyalar na gore daha yuksek kapasite, yuksek C oranlar ve du suk i c diren c sunmaktad r. Fakat lityumun reaktif yap s ce sitli problemlere yol a cmaktad r. Bu kimyasal maddeyi bir paket i cerisinde muhafaza etmek, so gutmak, kontrol etmek ve takibini sa glamak olduk ca zordur. Bu sebeplerden oturu Batarya Yonetim Sistemleri hakk ndaki akademik cal smalarda pilin sarj, omur ve gu c durumunun takip edilmesine onemle yer verilmitir. Bu tezde lityum tabanl pillerin sarj durumunun yuksek hassasiyet ile takibine odaklan lm st r. Bir pilin a c k devre konumunda iken sarj durumu tespiti yap lmas olduk ca kolayd r fakat yuk alt ndaki bir pilin sarj durumunun kestirilmesinde ce sitli zorluklar ortaya c kar. Literaturde Kalman tabanl ltreler, ozellikle EKF ve ADEKF, en iyi SOC kestirimi performans n sa glamaktad r. EKF du suk hesaplama karma s kl g sunar fakat yak nsama zaman uzundur. Di ger taraftan, ADEKF k sa yak nsama zaman sunarken yuksek hesaplama karma s kl g na sahiptir. Bu tezde bu iki algoritman n gu clu yonlerini dengeleyen yeni bir PID kontrollu EKF algoritmas sunulmu stur. Ayr ca bu tez, yak nsama performans n n onemini de vurgulamaktad r.

Kaynağa git Özyeğin Üniversitesi

Aramaya Dön

Özyeğin Üniversitesi

Kaynağa git

State of charge estimation for lithium-based batteries

Yazar Çelik, Mert

Basım Tarihi 2020-01

Konu SOC estimation, Extended kalman filter, Convergence time, Computational time

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Kayıt Numarası 66e616e9-4714-4bf4-a3d5-70f844768179

Lokasyon Department of Electrical and Electronics Engineering

Tarih 2020-01

Örnek Metin This thesis proposes a new State of Charge (SOC) estimation method for lithiumbased batteries, which o ers a good trade-o between convergence and computation times. Lithium-based battery packages are quite common in the automotive industry and beyond because of their high-power density and dynamic response capabilities. Per a given volume, lithium-based battery cells have much more capacity, higher Crates, and lower internal resistance than other cell chemistries. However, this comes at a cost because of lithium's reactive nature. It is hard to preserve, monitor, cool, and control lithium in a pack within a safe state. For these reasons, battery control, or in other words, Battery Management Systems (BMS) is a major topic in the literature, and estimation of SOC, State of Health (SOH), and State of Power (SOP) are considered as core subfunctions of BMS. This thesis focuses on improving SOC estimation for lithium-based batteries. SOC estimation determines the remaining charge level on the battery and is very critical for battery-powered devices. This process is relatively straightforward when the battery is in the resting state. However, it can be di cult while the battery-powered device is operating, due to process disturbances and model uncertainties. The best performing SOC estimation methods in the literature are based on Kalman Filtering, and they are specifically Extended Kalman Filter (EKF) and Adaptive Dual Extended Kalman Filter (ADEKF). While EKF offers the shortest computation time, it results in a long convergence time. On the other hand, ADEKF o ers short convergence time and long computation time. We propose PID-controlled EKF, which o ers a mid-point in terms of convergence and computation times. The importance of convergence characteristics are also articulated in this thesis, especially from an automotive perspective., Bu tez Lityum tabanl piller i cin yak nsama performans ve hesaplama karma s kl g dengelenmi s yeni bir SOC kestirim algoritmas sunmaktad r. Lityum bazl piller yuksek enerji yo gunlu guna ve dinamik tepkilere sahip oldu gu i cin endustride olduk ca yayg n olarak tercih edilmektedir. Bu piller, belirli bir hacimde di ger hucre kimyalar na gore daha yuksek kapasite, yuksek C oranlar ve du suk i c diren c sunmaktad r. Fakat lityumun reaktif yap s ce sitli problemlere yol a cmaktad r. Bu kimyasal maddeyi bir paket i cerisinde muhafaza etmek, so gutmak, kontrol etmek ve takibini sa glamak olduk ca zordur. Bu sebeplerden oturu Batarya Yonetim Sistemleri hakk ndaki akademik cal smalarda pilin sarj, omur ve gu c durumunun takip edilmesine onemle yer verilmitir. Bu tezde lityum tabanl pillerin sarj durumunun yuksek hassasiyet ile takibine odaklan lm st r. Bir pilin a c k devre konumunda iken sarj durumu tespiti yap lmas olduk ca kolayd r fakat yuk alt ndaki bir pilin sarj durumunun kestirilmesinde ce sitli zorluklar ortaya c kar. Literaturde Kalman tabanl ltreler, ozellikle EKF ve ADEKF, en iyi SOC kestirimi performans n sa glamaktad r. EKF du suk hesaplama karma s kl g sunar fakat yak nsama zaman uzundur. Di ger taraftan, ADEKF k sa yak nsama zaman sunarken yuksek hesaplama karma s kl g na sahiptir. Bu tezde bu iki algoritman n gu clu yonlerini dengeleyen yeni bir PID kontrollu EKF algoritmas sunulmu stur. Ayr ca bu tez, yak nsama performans n n onemini de vurgulamaktad r.