Lithium-ion (Li-ion) batteries have become one of the most popular energy storage technologies that have already revolutionized the global transportation and energy sectors with wide range of viable applications. In all Li-ion battery-powered applications, the battery management system (BMS) is one the main functionalities whose operation dictates the battery performance, safety and lifetime. Being the brain of the battery pack, the BMS typically monitors the states and conditions of the battery for user communication and to prevent the battery from being exposed to extreme conditions.
One of the challenges in BMS operation is related to accessing the battery health conditions, the state of health (SOH) and remaining useful life (RUL) correctly throughout the battery life. As the battery degrades, the internal aging mechanisms and their mixed effect and rate of evolution make the battery age, affecting the SOH and RUL which, in turn, affects on the estimation of other battery states (e.g. state of charge – SOC, and state of power – SOP). The aging mechanisms are not easily accessible by the today’s BMS monitoring capabilities, making it difficult to estimate SOH and RUL correctly and reliably. Problems related to SOH and RUL estimation inaccuracies are typically realized at the latter half of the Li-ion battery’s lifecycle, potentially causing either premature malfunction of the battery, or unnecessary retirement of a completely functional and healthy battery. Therefore, accurate and reliable condition monitoring in the BMS is one of the key features that needs to be addressed in next-generation BMS technologies in order to maximize the performance, lifetime and techno-economic viability of Li-ion batteries and applications.
The goal of this research project is to maximize the achievable service life of Li-ion batteries and their applications by utilizing real-time impedance-based monitoring and state estimation algorithms. These methods potentially offer significantly better accuracy and long-term reliability compared to traditional algorithms, enabling BMS to optimize battery usage more effectively and thereby maximize service life in primary applications, such as electric vehicles. Furthermore, these methods ensure precise knowledge of the battery's condition upon its retirement from electric vehicle service. This, in turn, facilitates realization of battery second-life applications that can significantly extend the operational lifespan and improve the environmental sustainability of Li-ion batteries. This highly innovative project holds significant scientific, economic, and societal potential and, if successful, it can produce groundbreaking research results that are also viable for real-world battery applications with commercial dimension.
The project focuses on utilizing real-time electrochemical-impedance-spectroscopy (EIS) measurement technology to be physically implemented in BMS, and in accessing the battery health-related and other condition metrics via the battery impedance. Besides integration of the technology into real onboard BMS environment, important research actions for the project also includes impedance data generation from real experiments, mapping/modeling the impedance data to the actual battery conditions, and derivation of the corresponding state estimation algorithms. Finally, the project methodology will be demonstrated with real battery systems and BMS to validate its safety, techno-economic viability and sustainability benefits.
The project is run at Tampere University (TAU) by Academy Research Fellow Jussi Sihvo. The project have also collaborators Prof. Daniel Stroe (AAU, Denmark) and Prof. Vaclav Knap (CTU, Czech Republic) whose role is emphasized in providing valuable data generation, testing and validation resources. The project is funded by the Research Council of Finland, and its duration is four years between 2025-2029.
Funding
Contact persons
Jussi Sihvo, Academy research fellow, jussi.sihvo [at] tuni.fi