Abstract: Rapid and accurate estimation of the state of health（SOH） of lithium-ion batteries throughout their entire life cycle can help improve the safety and reliability of energy storage equipment. An SOH estimation model is proposed, which combines indirect health indicators（IHIs） with bi-directional long short-term memory（Bi LSTM） network optimized by the whale optimization algorithm（WOA）. The model takes into account the influence of future states on the current SOH. First, the constant current-constant voltage charging and discharging process of lithium-ion battery is analyzed, multiple time characteristics of voltage, current, and temperature that dynamically change with charging and discharging cycles are extracted as IHIs, and the indicator of discharging load voltage drop time is added. Then, through correlation analysis, selected IHIs with high correlation to capacity are set as input features. Finally, a Bi LSTM network optimized by WOA is established as the battery SOH estimation model, and the NASA lithium-ion battery dataset is used to estimate the battery SOH under two different operating conditions. The results indicate that the proposed method can effectively improve the estimation accuracy of SOH.