為了因應淨零碳排放的目標，電動載具尤其是電動車的需求漸增，且為了滿足鋰離子電池快充又能延長電池壽命，本研究以四階段定電流充電法為基礎，根據電池健康度與內電阻來建立電池模型，用以模擬四階段定電流充電時間、充完電量與充電過程能量損失等三種目標參數，並藉由粒子群演算法進行搜尋四階段最佳充電電流組合。本文先以電池初始電量20%為例，比較上限電壓和剩餘等份電量兩種切換機制於充放電循環後對電池老化之影響，接著評估當電池的初始電量與健康度為隨機時，使用粒子群演算法進行多目標分析來尋找最佳充電電流組合。由上限電壓切換機制之結果顯示，實驗組與對照組在同樣循環次數下，因電池健康度與內電阻會影響充電電流值，變動充電電流實驗組與固定充電電流對照組在同樣循環次數時，實驗組雖增加12.13%充電時間，但節省24.5%的能量損失與增加0.26%充電電量。於35次充放電循環下，實驗組相較於對照組延緩2.05%健康度的衰退，且以半經驗容量模型所預估鋰離子電池健康度80%汰換標準下，實驗組多出約121次充放電循環次數。由剩餘等份電量切換機制之結果顯示，在同樣循環次數下，實驗組可節省21.15%的能量損失但會增加10.17%的充電時間，且於30次充放電循環下，實驗組延緩1.52%健康度衰退，可多出約105次充放電循環次數。最後，由隨機初始電量與健康度之結果顯示，在初始電量16.8%與健康度91.18%情境和初始電量37.4%與健康度91.56%情境下，剩餘等份電量目標函數結果較佳，在初始電量67.4%與健康度92.97%情境和初始電量87%與健康度92.31%情境下則是上限電壓結果較佳。

The target of net-zero emissions and demand for electric vehicles is booming. To find a method fulfill fast charging and extend Lithium-ion battery life. This study establishes a model based on battery health and internal resistance to simulate three parameters including charge time, charge capacity, and energy loss using a four-stage constant current (4SCC) charging method. Then, a particle swarm algorithm (PSO) is used to search for the optimal charging current combination. First of all, takes an initial state-of-charge (SoCini) 20% as an example to compare the impact of the two switching mechanisms of upper limit voltage (Vlimit) and equally discretized the remaining SoC intervals (EDR-SoC) on battery aging after charge and discharge cycles. Then, a search of multi-objective PSO charging current was analyzed under random SoCini and state-of-health (SoH). For the Vlimit strategy, comparing the experimental group with the control group for the same cycle numbers, the charge time, energy saving, and charge capacity had an increase of 12.13%, 24.5%, and 0.26%, respectively. Moreover, the experimental group was able to extend the capacity degradation of 2.05% after 35 cycles as compared to the control group, and it had an increase of about 121 more cycles when employing the semi-empirical capacity model for predicting the battery life ended within 80% SoH. For EDR-SoC strategy, the charging time increased by 10.17% and the energy saving increased by 21.15%. After 30 cycles, the experimental group was able to extend the capacity degradation of 1.52% as compared to the control group corresponding to 105 more cycles. Furthermore, under random SoCini and SoH conditions, EDR-SoC was better than Vlimit at SoCini 16.8%, SoH 91.18% and SoCini 37.4%, SoH 91.56%. Then Vlimit was better than EDR-SoC at SoCini 67.4%, SoH 92.97% and SoCini 87%, SoH 92.31%.

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