Numerous deficiencies of the conventional BMS impair the development of an efficient and long-life battery system. The information required for BMS is restricted either to measurement or indirect estimation. The latter might be erroneous due to the accumulated error in the measurement and over-simplified models to achieve high computational efficiency. In particular, the measurement is limited to the cell voltage, cell current, and surface temperature. These data are typically used for estimation of the internal parameters of the battery cell. Unfortunately, the inner coupled electrochemistry and physics determine the performance, safety, and longevity of batteries which are directly connected to the internal measurable parameters such as temperature, electrode potential, internal pressure, strain, and gas formation. The lack of inner information hinders the accurate judgement of the status of inner active components. This is recognized as a primary challenge for the present BMS technologies [1]. Thus, the sensing of battery cells has been moving from traditional external signals towards the internal measurement [2]. Although the development of a battery cell with the internal sensors simplifies the battery control, it is challenged by two issues: First, integration of the sensors into the electrode layers impacts the performance, safety and lifetime of the battery. Second, the implementation of the communication from the sensor to the BMS comes with the intrusion into the cell and jeopardizes the cell safety. This paper provides a synopsis of the available technologies to realize internal sensing of the lithium-ion batteries. An overview of the communication techniques that are used for the purpose of internal sensing is presented. Sensor technologies that have been used in the internal sensing of batteries such as fiber optics, flexible and printable sensors are reviewed. Finally, the co-estimation techniques which can be used to reduce the sensor integration efforts are summarized. In conclusion, the paper delineates future perspective and research directions.

[1] Z. Wei, K. Liu, X. Liu, Y. Li, L. Du, and F. Gao, “Multi-level data-driven battery management: From internal sensing to big data utilization,” IEEE Transactions on Transportation Electrification, 2023, doi: 10.1109/TTE.2023.3301990.
[2] Z. Wei, J. Zhao, H. He, G. Ding, H. Cui, and L. Liu, “Future smart battery and management: Advanced sensing from external to embedded multi-dimensional measurement,” Journal of Power Sources, vol. 489, p. 229462, 2021, doi: 10.1016/j.jpowsour.2021.229462.