Abstract: Accurate, continuous, and efficient estimation of the state of health(SOH) for lithium-ion batteries is crucial for the safe and effective operation of electric vehicles. However, in engineering practice, the fragmented charging and discharging behavior can only generate fragmented data, and only contain conventional parameters such as voltage and charge capacity, which brings obstacles to the existing methods. To address the aforementioned challenges, an adaptive battery SOH estimation system based on charging voltage segments is proposed, which integrates three sub-methods to achieve continuous and accurate SOH estimation under arbitrary charging segments. Firstly, the highest-priority optimal segment estimation method is established within the adaptive estimation system, exploring the accurate estimation of battery SOH using a single charging segment. Based on correlation analysis and capacity degradation characteristics, the optimal voltage segment is defined, achieving a minimal RMSE of just 0.012. Secondly, considering the limitation of incomplete optimal features in practical engineering, which may lead to estimation failure, a sliding sampling estimation method is designed. This method enables continuous SOH estimation under arbitrary charging segments, achieving an optimal RMSE of 0.017. Finally, to balance estimation accuracy and continuity in the adaptive SOH estimation system, a neighboring-cycle combination estimation method is proposed based on the short-term stability of SOH. By integrating information from adjacent charging cycles and combining segments to simulate the optimal segment, this method achieves an optimal RMSE of only 0.014. The three sub-methods support and complement each other in estimation accuracy, robustness and stability, and provide a strong guarantee for the adaptive estimation system. The research findings presented hold significant importance and offer valuable insights for the engineering application of battery intelligent management and state estimation technologies.