本論文提出一基於庫侖電量累計法，藉由校正電池健康狀態進行優化之蓄電池電量狀態線上估測法。本方法不需仰賴取得不易的電池特性資料庫或具高度運算能力的控制器，僅利用廠商出具於規格表的資訊，便可執行精確的蓄電池電量線上估測。為達成準確的電池電量估測，須將電池之健康狀態納入考量。利用本論文提出之完全校正程序與部分校正程序，將電池定期地操作於預設之充放電電流，可更新電池的滿容量，校正其健康狀態。其中，為使具不同健康狀態的電池有合理的校正標準，電池之充放電率皆須以當下的滿容量進行正規化。為排除電流量測誤差造成的影響，將滿足充電截止條件之電池的電量狀態修正為100%，滿足放電截止條件之電池的電量狀態修正為0%。其後，電池之充放電率便可依更新之電池健康狀態進一步調整。實驗結果顯示，本論文所提之方法在刻意置入0.3%的電流量測誤差下，經數次的健康狀態校正後，電量估測誤差皆低於1.905%。
另一方面，本論文亦以四組降昇壓式電池電源模組串聯組成之電池電源系統驗證所提出的電量估測法。其中，每個電池電源模組皆由一雙向降昇壓式轉換器與一磷酸鋰鐵電池組構成，可獨立控制，共同運轉，滿足負載需求或充電條件。此系統可利用完全與部分校正程序，檢測出的電池最大可釋放容量，更新電池健康狀態，提升電池電量估測的準確度，並依據本估測法得到的電池電量狀態，個別調控電池充放電電流，執行電池電量平衡。再者，藉由控制電池電源模組中的閘極驅動訊號，可在不需額外的機械開關，且不打斷系統運轉的情況下，隔離電量耗盡或已充飽的電池。實驗結果證實，此電池電源系統可配合適當的充放電策略，提供準確的電池電量線上估測，並於系統充放電運轉時執行電池電量平衡、容錯機制、與輸出電壓調控。

This dissertation is focused on the improvement of state-of-charge (SOC) estimation with the coulomb counting method for rechargeable batteries by means of state-of-health (SOH) calibration. The proposed approach intends to provide an easy-to-use solution for on-line indication with high accuracy to estimate the battery status without the need of demanding calculations or hard-earned databases. To estimate the SOC of an aged battery more accurately, the degradation of its full capacity has to be taken into account. By scheduling the battery’s charging/discharging current and monitoring the battery’s status, the existing full capacity can be updated regularly by normal calibration or occasionally by partial calibration, in which the charging/discharging rates are normalized with the latest updated full capacity to agree with the battery’s statuses. To exclude the estimation error caused by an inaccurate current, the SOC is reset to 0% when the battery is completely exhausted and 100% for a fully charged battery. With an updated SOH, the battery C-rate is re-scaled accordingly. Experimental tests are carried out to demonstrate that the proposed method can provide an accurate online indication of batteries’ SOCs. With an implanted error of 0.3% in current measurement, the SOC estimation error can always be less than 1.905% after a number of SOH calibrations.
On the other hand, a laboratory battery power system is set up by four buck-boost type battery power modules (BPMs) with the lithium iron phosphate (LiFePO4) batteries to verify the proposed SOC estimation method with SOH calibration. During the operation of the battery power bank, the BPMs are controlled individually and also work collaboratively to meet the load requirements or sharing the charging power from the DC source. Charge equalization among batteries can be executed by individually controlling the currents either into or out from the batteries in accordance with the real-time SOCs, estimated with the proposed estimation. By identifying the maximum charge status with normal or partial calibration, the SOHs of the batteries in BPMs can be updated accordingly, and then the SOCs can be estimated more precisely. Moreover, without interrupting the system operation, the exhausted or damaged battery can be isolated simply by turning off its corresponding active power switch of the BPM without the need of an extra mechanical switch. Experimental tests are carried out to demonstrate that the battery power system can provide an accurate online indication of batteries’ SOCs with properly programmed discharging/charging scenarios to conduct charge equalization, output voltage regulation, and fault tolerance throughout discharging/charging processes.

摘 要 iii
Abstract v
Table of Contents vii
List of Figures ix
List of Tables xi
List of Nomenclature xii
Chapter 1 Introduction 1
1-1 Research Background and Motivation 1
1-2 Contributions 5
1-3 Content Arrangement 6
Chapter 2 Overviews and Applications of Rechargeable Batteries 7
2-1 Introduction to Rechargeable Batteries 7
2-2 Charging Methods 10
2-3 Conventional Applications 14
2-4 Charge Equalization Schemes 17
2-4-1 Energy Dissipation 18
2-4-2 Energy Transfer 19
2-4-3 Energy Control 22
2-5 Battery Power Modules 24
2-5-1 Concept of Battery Power Modules 24
2-5-2 Configurations of Battery Power Bank with BPMs 26
2-5-3 Design Example with Experimental Results 28
Chapter 3 SOC Estimation and SOH Evaluation 32
3-1 Related Definitions 32
3-2 SOC Estimation Methods 37
3-2-1 Open-Circuit Voltage (OCV) Measuring 37
3-2-2 Internal Resistance Measuring 38
3-2-3 Loaded Voltage Measuring 40
3-2-4 Coulomb/Ampere-Hour Counting 41
3-3 SOH Evaluation Methods 43
3-3-1 Electrochemical Impedance Spectroscopy (EIS) 43
3-3-2 Adaptive Filter Algorithm 45
3-3-3 Learning Algorithm 46
Chapter 4 SOC Estimation with SOH Calibration 50
4-1 Charging and Discharging Rates 50
4-2 Current Measurement Error 53
4-3 SOH Calibration Procedures 56
4-3-1 Normal Calibration 56
4-3-2 Partial Calibration 58
4-3-3 Control Scenario of SOC Estimation with SOH Calibration 61
4-4 SOC Estimation Error 62
4-5 Verification of SOC Estimation with SOH Calibration 63
Chapter 5 Experimental Verifications with Battery Power Modules 74
5-1 Battery Power System with Series Buck-Boost Type BPMs 74
5-2 Control Scenarios for Charging/Discharging Phases 78
5-3 Experimental Verifications 84
5-4 Fault Tolerance 92
5-5 Comparison of Battery Energy Utilization 95
Chapter 6 Conclusions and Discussions 99
6-1 Conclusions 99
6-2 Discussions 100
6-3 Future Research 103
References 105
Publication List 112

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