隨著台灣再生能源比例逐漸提升，以及台電電力交易平台與輔助服務機制的推動，儲能系統(ESS)在電力調度中的角色日益重要。另一方面，鋰離子電池因具備高能量密度與良好的循環性能，已廣泛應用於電動車與儲能場域；然而，其回收率仍有限，若能透過更合理的充電策略延長使用週期，將有助於降低電池汰換與資源消耗。因此，本研究嘗試結合貝葉斯優化與 LSTM，建立鋰離子電池充電控制模型，並於低成本嵌入式平台上進行實作與初步驗證，以評估智慧充電策略在實務應用中的可行性及其限制。
本研究使用專業充放電測試機台，收集不同充電條件下的電壓與電流變化與容量，進行評估鋰電池的壽命。採用RNN-LSTM序列模型，針對電流與電壓等因素進行評估，並透過模型的預測結果，調整電流大小以減少消耗並提升鋰離子電池的循環壽命。
實驗中透過多次循環測試，觀察每組充電電流對於容量的損失的影響，評估最有效的充電方式，期望能有效延緩電池老化，降低電池報廢的數量，減少對環境的污染並為實現淨零碳排放的目標盡一份力量。

With the increasing penetration of renewable energy in Taiwan and the development of Taiwan Power Company’s electricity trading platform and ancillary service mechanisms, energy storage systems(ESS) have become increasingly important in power system operation. Meanwhile, lithium-ion batteries are widely used in electric vehicles and energy storage applications due to their high energy density and long cycle life. However, their limited recycling rate highlights the need for strategies that can extend battery lifespan and reduce resource consumption.
This study combines Bayesian Optimization and Long Short-Term Memory(LSTM) networks to develop a charging control model for lithium-ion batteries. A professional battery cycler was used to collect charging and discharging data under different operating conditions, including voltage, current, and capacity variations. An RNN-LSTM model was then employed to evaluate battery conditions and dynamically adjust charging current based on prediction results.
Through repeated cycling tests, the effects of different charging currents on capacity degradation were analyzed to identify more effective charging methods. The proposed strategy was implemented on a low-cost embedded platform for preliminary validation. The ultimate goal of this research is to mitigate battery aging, reduce battery waste, decrease environmental impact, and contribute to the achievement of net-zero carbon emissions.

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