Success of electric mobility and connected future depends on advanced high capacity Lithium-ion batteries and a tailored battery management system that keeps track of battery health and safety to optimize the performance. The drive for advanced batteries is being pursued on one front via the development of novel chemistry, for example, blended composite cathodes to achieve enhanced performance and life. On the other front, the need for optimal battery performance via operational control viz. a robust Battery Management System (BMS) that accurately predicts state-of-charge (SOC), state-of-power (SOP) and state-of-health (SOH) in a computationally efficient way, has been lacking in many ways due to the reliance on simple and fast-to-compute models such as equivalent circuit models (ECM) that lack the accuracy needed for large battery packs. This paper reports an on-board reduced-order electrochemical thermal model (ROTM) based SOC and voltage estimation for a 12S1P configuration composite-cathode battery pack and demonstrates its practicality by implementing it on four different micro-controller units (MCU) (ATmega:2560&328, Infineon:TC275&TC297). We show that on-board ROTM based SOC and voltage prediction have better accuracy compared to the conventional ECM based methods under both static and dynamic load conditions.

中文翻译：

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电动汽车的车载状态估计：通过电化学模型实现准确性和计算效率

电动汽车和互联未来的成功取决于先进的高容量锂离子电池和定制的电池管理系统，可跟踪电池健康和安全以优化性能。通过开发新型化学物质，例如混合复合阴极，以实现更高的性能和寿命，正在推动先进电池的发展。另一方面，需要通过操作控制实现最佳电池性能。一个强大的电池管理系统 (BMS) 以计算效率高的方式准确预测荷电状态 (SOC)、功率状态 (SOP) 和健康状态 (SOH)，但在许多方面一直缺乏依赖于简单且快速计算的模型，例如缺乏大型电池组所需精度的等效电路模型 (ECM)。本文报告了基于 12S1P 配置复合阴极电池组的机载降阶电化学热模型 (ROTM) 的 SOC 和电压估计，并通过在四个不同的微控制器单元 (MCU) (ATmega: 2560&328，英飞凌：TC275&TC297）。我们表明，在静态和动态负载条件下，与传统的基于 ECM 的方法相比，基于车载 ROTM 的 SOC 和电压预测具有更好的准确性。