Solid-state batteries (SSBs) are emerging as a promising alternative to conventional lithium-ion batteries due to their superior safety, energy density, and lifespan. However, understanding their thermal behavior under dynamic operating conditions is crucial for ensuring safety and performance, especially in e-mobility applications. This study presents a comparative thermal analysis of SSB and lithium nickel cobalt aluminum oxide (NCA) 21700 cells during charging under various ambient temperatures (0 °C, 25 °C, and 40 °C). Key metrics such as temperature gradients (ΔT/Δt) and differential temperature rise (ΔT) are evaluated to identify critical thermal behaviors. The results reveal that SSBs exhibit significantly higher ΔT and ΔT/Δt. While battery management systems (BMS) typically regulate absolute temperature rise (ΔT), this study highlights the importance of monitoring ΔT/Δt as a critical parameter for mitigating accelerated degradation and preventing thermal runaway. The findings contribute valuable insights toward developing robust thermal management strategies for next-generation battery systems.

State-of-the-art lithium-ion battery state estimation techniques, including state-of-charge, state-of-health, and remaining useful life in battery management systems (BMS), are extensively dependent on equivalent electric circuit model (ECM) parameters. ECM parameters are subject to change due to battery aging, changes in operating temperature, and other operating conditions. Therefore, updating the model parameters in real-time by on-board BMS is essential to maintain the accuracy of state estimation where ECM is used as the primary building block. ECM parameters obtained in laboratory conditions cannot guarantee accuracy in onboard BMS throughout the entire life cycle of an electric vehicle battery. Currently, onboard state estimation techniques use static ECM parameters due to complexity and computational time in parameterization and online computation, especially with higher-order ECMs. Therefore, this paper presents a comparative analysis of 2-RC, 3-RC, and 4-RC ECMs concerning parameterization time, computational cost, and accuracy, providing an understanding of the possible integration of dynamic ECM models through regular parameterization in onboard BMS. The sensitivity analysis depicts that the 3-RC model would be most appropriate for considerably high accuracy for a BMS with moderate computational power, ensuring safe and reliable state estimation throughout the lifetime of the battery. Moreover, with the changes in ambient conditions, as the sensitivity of R2and C2are much lower compared to other parameters thus, these do not need update with the changes in operating temperature.

Rapid printed circuit board (PCB) development for modular battery management systems (BMS) can provide a flexible, scalable, and customizable solution for all lithium-ion battery-powered devices as well as in research and development. Therefore, to satisfy the increasing demand and stringent requirements of high performance, low cost, and smaller footprints, a single-stage PCB development technique using a CO2laser, galvo scanner, and two-axis computer numerical control (CNC) router is proposed in this paper. The system can selectively cut/erode between the copper or substrate layers with only a single active laser by controlling the intensity of the laser beam. The proposed method circumvents the fabrication of a negative and the chemical processing steps of PCB manufacturing, resulting in higher production speed at a reduced cost. The technique is also environmentally friendly and suitable for small-batch production to support small industries and research. The superiority of the proposed method is also demonstrated by a comparison between the proposed and a high-density fiber laser-based method.

The increasing events of fire and catastrophic failure of lithium-ion batteries (LIBs) due to inaccurate thermal information or improper thermal management based on surface temperature data only, once again indicates the necessity of accurate core temperature information. In view of this, a rapid, more convenient but accurate thermal modeling technique for LIB is introduced in this paper using SIMBA. SIMBA is a powerful new generation power electronics simulation software powered by Python. A second-order electro-thermal model-based core temperature estimation scheme is developed in SIMBA. A wide range of battery test data is used for experimental validation of the model. Further, four standard drive cycle profiles are used to assess the impact of discharge current on the core temperature of LIB. The electro-thermal model allows estimating the core temperature from external measurements including voltage, current, and surface temperature without installing a physical core temperature sensor which is practically challenging. The proposed modeling technique is extremely convenient and the model is computationally efficient and simple enough to be implemented in practical purpose lithium-ion battery management systems.