Q&A with Mathias Fritzson: Solving challenges in battery
In this blog post, Mathias Fritzson, Product Manager for Siemens Capital Embedded software products, shares valuable insights into the evolving challenges of BMS
For BMS applications, vast datasets containing vital parameters of the battery pack, 14,15 such as real time current, voltage, temperature, and states of each component are generated which require data storage capabilities. These datasets can be stored for analysis and performing computational studies in remote cloud servers.
The increasing energy density of lithium-ion batteries leads to increasing safety requirements in battery systems, especially in mobile applications such as urban air mobility or drone applications. These requirements can be addressed with adapted sensors and actuators, such as low-cost temperature sensors or high-power antifuses.
A multi-layered BMS architecture can leverage edge and cloud computing for enhanced functionality.24 The architecture includes end sensing for local data acquisition, edge computing for time-critical monitoring, and cloud computing for scalable, real-time data analysis.
Hence, a typical BMS was conceptualised in the early 1990s with functionalities to monitor and control operation by measuring and estimating parameters and states at the cell, module, and pack level to ensure safe usage and prolonged life.
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