Lead–acid batteries used in micro-hybrid applications with stop–start and recuperative braking functionality are experiencing increased energy throughputs under the high-rate partial state-of-charge regime (HRPSoC) compared with conventional starter-batteries. To ensure more reliable operation, the batteries are installed together with an intelligent battery monitoring system, which performs state-of-charge (SoC), state-of-function (SoF) and state-of-health (SoH) estimation. The SoH is usually defined through capacity loss and resistance increase due to ageing processes. This value is used to adapt the SoC estimation and detect the need for battery exchange. Furthermore, resistance change over lifetime is auxiliary for estimating SoF, thus information about capacity loss results in a more accurate on-board status diagnostic. Increasing resistance correlates to capacity degradation, but quantification and qualification of this correlation is difficult. Cycle counting, as another method to determine SoH, requires extensive tests to cover all dependencies, which come with cyclic ageing. These methods are less feasible for micro-hybrids especially because the partial SoC condition leads to stronger sulfation, which not directly depends on energy throughput, but causes significant capacity loss and impairment of chargeability. The detection and quantification of sulfation would determine this substantial part of capacity loss in micro-hybrids. An algorithm developed for micro-hybrid applications is presented. It estimates capacity loss due to sulfation using open-circuit voltage (OCV) measurements and charged ampere-hours over prolonged charging periods. Additionally, changes in the OCV–SoC relationship and water-loss are considered, since these effects also influence the battery capacity. This development allows a better on-board diagnostic of battery state in modern micro-hybrid applications.
Institute for Power Electronics and Electrical Drives (ISEA), RWTH Aachen University
Research associate and PhD student
Biography: Monika Kwiecien graduated as a Dipl.-Ing. in electrical engineering from RWTH Aachen University, Germany. Afterwards she worked in the University as a research assistant at the Philips Chair for Medical Information Technology. In 2013, she joined the Institute for Power Electronics and Electrical Drives (ISEA) at RWTH Aachen University, as a member of the scientific staff and a Ph.D. student. Her research topic is the development of new algorithms for the diagnostic of lead–acid batteries in vehicle applications.