基于BNN-PF的卫星锂离子电池多工况SOH估计

BNN-PF-based SOH estimation of satellite lithium-ion batteries under different operating conditions

利用在轨可测参数准确估计卫星锂离子电池的健康状态,对卫星的安全可靠运行至关重要。对于卫星锂离子电池的健康状态估计,性能退化表征和评估都需要适应多种工况。针对卫星锂离子电池在多种工况下,模型和数据中的不确定性带来的表征参数的有效性和评估结果的可靠性不足的问题,本文提出了一种基于BNN-PF的概率性健康状态估计方法。从卫星锂离子电池充电过程的可测参数中提取不同的健康因子来表征性能退化,将排列熵与主成分分析法相结合,提高特征对不同任务的适应能力。在此基础上,采用贝叶斯神经网络估计锂离子电池的健康状态并量化不确定性,基于粒子滤波算法融合经验模型得到的不确定性,进一步增强了所提方法对多工况的适应性。实验结果表明,本文所提方法对多工况下的卫星锂离子电池健康状态估计具有良好的适应性和通用性。交叉验证试验结果显示,最大估计误差小于0.01,且多数结果的估计区间覆盖率大于0.95,表明方法在空间应用场景下具有良好的前景。

Accurately estimation of the state of health(SOH)of satellite lithium-ion batteries using in orbit measurable parameters is crucial for the safe and reliable operation of satellites.For the SOH estimation of satellite lithium-ion batteries,both performance degradation characterization and assessment should be self-adaptive to different operating conditions.Therefore,to address the insufficient validity of characterization parameters and the reliability of assessment results caused by uncertainty in the model and data for satellite lithium-ion batteries under different operating conditions,this paper proposes a probabilistic SOH estimation method based on BNN-PF.Extracting different health indicators(HI)from measurable parameters during satellite lithium-ion batteries charging process to characterize performance degradation,combining Permutation Entropy with principal component analysis to improve feature recognition ability for different tasks.Furthermore,a Bayesian neural network(BNN)is applied to infer the SOH of lithium-ion batteries and quantify uncertainty.The uncertainty obtained by integrating empirical model with particle filter(PF)algorithms further enhances the adaptability of the proposed method to different operating conditions.The experimental results illustrate that the method proposed in this paper demonstrates good adaptability and universality for SOH estimation of satellite lithium-ion batteries under different operating conditions.The cross-validation test results show that the maximum estimation error is less than 0.01,and the estimation interval coverage of most results is higher than 0.95,indicating that the method has good prospects in spatial application scenarios.