鋰離子電池已被廣泛運用到如手機、筆電等各種可攜式產品以及如電動腳踏車、電動汽車等各類的電動載具中，因此這些產品都需要良好的充電器以提高電池性能。然而，商業化產品通常偏好簡單型的充電法則，且未考量不同電池健康狀態(State of Health, SOH)對充電演算法之需求。有鑑於此，本文在不同的電池SOH狀況下實測五種簡單易實現的充電法，並且根據六項充電性能指標選擇出不同SOH下最適用之充電法。本文比較的六項充電性能指標包含最大溫升(取倒數)、平均溫升(取倒數)、充電容量、放電容量，充電速率以及充電效率，而所考慮之五種簡易型充電法則分別為三種不同截止端電壓之定電流定電壓充電法、定功率定電壓(Constant Power-Constant Voltage, CP-CV)充電法以及定損失定電壓(Constant Loss-Constant Voltage, CL-CV)充電法。除了選擇適用於不同SOH之充電法之外，本文亦針對所實現之五種充電法提出適用的SOH估測法則，該演算法選擇兩特徵參數，包括充電時電壓由3.7 V升至4.1 V所需的時間以及充電時電壓由3.8 V升至4.1 V所需的時間，並且利用類神經網路來建立電池SOH估測器，測試結果之最大相對誤差為4.12%，最小相對誤差為0.1%，平均相對誤差為0.98%，均方根誤差為1.35，此可證明所提之SOH估測機制可有效的估測鋰離子電池的健康狀態。

Lithium-ion batteries have been widely used in various portable devices such as mobile phones, laptops, as well as electric vehicles including electric bicycles and cars. Therefore, these products require high-quality chargers to enhance battery performance. However, commercially available products often prefer simple charging algorithms and do not take into account the different requirements of battery state of health (SOH) for charging algorithms. In light of this, this study experimentally tested five simple and easily implementable charging methods under three different battery SOH conditions: 100%, 95%, and 91%. Based on six charging characteristics, the most suitable charging method for each of these three SOH levels was selected. The six charging characteristics compared in this study include maximum temperature rise (reciprocal value), average temperature rise (reciprocal value), charging capacity, discharging capacity, charging rate, and charging efficiency. The five considered simple charging methods were three different constant current-constant voltage charging methods with different termination voltage settings, constant power-constant voltage charging method, and constant loss-constant voltage charging method.
In addition to selecting the appropriate charging methods for different SOH levels, this study also proposed suitable SOH estimation algorithms for these five charging methods. This algorithm selected two feature parameters: the time required for the voltage to increase from 3.7 V to 4.1 V during charging and the time required for the voltage to increase from 3.8 V to 4.1 V during charging. Neural networks were used to estimate the battery SOH, and the test results showed a maximum relative error of 4.12 %, a minimum relative error of 0.1 %, an average relative error of 0.98 %, and a root mean square error of 1.35. These results demonstrate the effectiveness of the proposed SOH estimation algorithm in accurately estimating the health status of lithium-ion batteries.

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