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首页> 外文期刊>International Journal of Electrochemical Science >A Novel Adaptive Particle Swarm Optimization Algorithm Based High Precision Parameter Identification and State Estimation of Lithium-Ion Battery
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A Novel Adaptive Particle Swarm Optimization Algorithm Based High Precision Parameter Identification and State Estimation of Lithium-Ion Battery

机译：基于高精度参数识别和锂离子电池的新型自适应粒子群优化算法

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摘要

Lithium-ion batteries are widely used in new energy vehicles, energy storage systems, aerospace and other fields because of their high energy density, long cycle life and high-cost performance. Accurate equivalent modeling, adaptive internal state characterization and accurate state of charge estimation are the cornerstones of expanding the application market of lithium-ion batteries. According to the highly nonlinear operating characteristics of lithium-ion batteries, the Thevenin equivalent model is used to characterize the operating characteristics of lithium-ion batteries, particle swarm optimization algorithm is used to process the measured data, and adaptive optimization strategy is added to improve the global search ability of particles, and the parameters of the model are identified innovatively. Combined with extended Kalman algorithm and Sage-Husa filtering algorithm, the state-of-charge estimation model of lithium ion battery is constructed. aiming at the influence of fixed and inaccurate noise initial value in traditional Kalman filtering algorithm on SOC estimation results, Sage-Husa algorithm is used to adaptively correct system noise. The experimental results under HPPC condition show that the maximum error of the model is less than 1.5%. Simulation results of SOC estimation algorithm under two different operating conditions show that the maximum estimation error of adaptive extended Kalman algorithm is less than 0.05, which realizes high-precision lithium battery model parameter identification and highprecision state-of-charge estimation.
机译：锂离子电池广泛用于新能源车辆，储能系统，航空航天等领域，因为它们的能量密度高，循环寿命长，高成本的性能。准确的等效建模，自适应内部状态表征和准确的充电状态是扩展锂离子电池应用市场的基石。根据锂离子电池的高度非线性操作特性，临时等效模型用于表征锂离子电池的操作特性，使用粒子群优化算法来处理测量的数据，并加以自适应优化策略来改进创新地识别了粒子的全球搜索能力和模型的参数。结合扩展卡尔曼算法和Sage-Husa滤波算法，构建了锂离子电池的充电状态估计模型。针对传统卡尔曼滤波算法在SOC估计结果上的固定和不准确噪声初始值的影响，Sage-Husa算法用于自适应地校正系统噪声。 HPPC条件下的实验结果表明，该模型的最大误差小于1.5％。两个不同操作条件下SOC估计算法的仿真结果表明，自适应扩展卡尔曼算法的最大估计误差小于0.05，实现高精度锂电池模型参数识别和高度充电估计。

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