State of charge (SOC) estimation is of vital importance in battery management systems to ensure both safety and reliability. However, the polarization effect, the stochastic disturbance and the highly nonlinear and dynamic natures throughout the whole lifetime of batteries bring many challenges for accurate online estimation. On account of these difficulties, this paper proposes a novel SOC estimation strategy based on open circuit voltage by means of some implementable fractional-order techniques. Firstly, the fractional-order equivalent circuit model that can reveal more intrinsic electrochemical characteristics of batteries is introduced and parameterized by particle swarm optimization algorithm, then it is combined with particle filter that based on Monte Carlo method for SOC estimation. To achieve rapid convergence and strong robustness, an adaptive noise variance updating algorithm is adopted to update the SOC estimations in particle filter. Moreover, considering the computational burden of fractional-order model, the infinite impulse response filtering technique that adapts to data-driven modeling is introduced to simplify the discrete state space model in estimation. Lastly, the proposed algorithms are implemented in static and dynamic experiments, and the results indicate that the aforementioned strategies can realize fast convergence and precise estimation with applicable calculations.

充电状态（SOC）估算对于确保安全性和可靠性的电池管理系统至关重要。然而，极化效应，随机扰动以及整个电池寿命中的高度非线性和动态特性为准确的在线估算带来了许多挑战。鉴于这些困难，本文提出了一种基于开路电压的新颖SOC估计策略，该方法采用了一些可行的分数阶技术。首先，通过粒子群优化算法引入并揭示了可以揭示电池更多固有电化学特性的分数阶等效电路模型，然后将其与基于蒙特卡洛方法的粒子滤波器相结合，用于SOC估计。为了实现快速收敛和强大的鲁棒性，采用自适应噪声方差更新算法更新粒子滤波器中的SOC估计。此外，考虑到分数阶模型的计算负担，引入了适用于数据驱动建模的无限冲激响应滤波技术，以简化估计中的离散状态空间模型。最后，在静态和动态实验中实现了所提出的算法，结果表明上述策略可以通过适用的计算实现快速收敛和精确估计。为了简化估计中的离散状态空间模型，引入了适用于数据驱动建模的无限冲激响应滤波技术。最后，在静态和动态实验中实现了所提出的算法，结果表明上述策略可以通过适用的计算实现快速收敛和精确估计。为了简化估计中的离散状态空间模型，引入了适用于数据驱动建模的无限冲激响应滤波技术。最后，在静态和动态实验中实现了所提出的算法，结果表明上述策略可以通过适用的计算实现快速收敛和精确估计。