An important issue towards cell individual battery monitoring is the employment of new sensor technologies and efficient algorithms to enlarge the diagnostic capabilities of battery systems. Especially for the onboard application in electric vehicles, impedance spectroscopy is one of the most promising methods to gather useful information about the battery. In this paper, a new framework for onboard impedance spectroscopy on cell level is developed, which incorporates a passive method without the need of an additional active signal generation. The derived estimator is based on the weighted overlapped segment averaging algorithm and is optimized for the application in a battery management system. The theoretical considerations are experimentally examined with a battery module consisting of parallel connected cells. Therefore, a battery test rig with high bandwidth is designed to enable a realistic replication of road driving scenarios. Statistical quality criterions for the online validation of impedance spectra using linear Kramers-Kronig tests are established and verified with corresponding experiments. A significant comparison with reference measurements based on the accuracy of estimated model parameters shows very good agreement. Next to model parameter estimation, diverse other application scenarios for the monitoring and diagnosis of battery systems based on impedance data are outlined.

电池单体电池监控的一个重要问题是采用新的传感器技术和有效的算法来扩大电池系统的诊断能力。特别是对于电动汽车的车载应用，阻抗谱是收集有关电池有用信息的最有前途的方法之一。在本文中，开发了一种用于单元级板载阻抗谱的新框架，该框架结合了无源方法，而无需额外的有源信号生成。导出的估算器基于加权重叠段平均算法，并针对电池管理系统中的应用进行了优化。理论上的考虑是通过使用由并联连接的电池组成的电池模块进行实验检验的。所以，具有高带宽的电池测试台被设计为能够真实复制道路行驶场景。建立了使用线性Kramers-Kronig测试进行阻抗谱在线验证的统计质量标准，并通过相应的实验进行了验证。与基于估计的模型参数准确性的参考测量值的显着比较显示出很好的一致性。除了模型参数估计之外，还概述了用于基于阻抗数据的电池系统监视和诊断的其他各种应用场景。与基于估计的模型参数准确性的参考测量值的显着比较显示出很好的一致性。除了模型参数估计之外，还概述了用于基于阻抗数据的电池系统监视和诊断的其他各种应用场景。与基于估计的模型参数准确性的参考测量值的显着比较显示出很好的一致性。除了模型参数估计之外，还概述了用于基于阻抗数据的电池系统监视和诊断的其他各种应用场景。