Madsen Anne K, Perera Darshika G
Department of Electrical and Computer Engineering, University of Colorado Colorado Springs, 1420 Austin Bluffs Parkway, Colorado Springs, CO 80918, USA.
Sensors (Basel). 2022 Aug 26;22(17):6438. doi: 10.3390/s22176438.
Efficient battery technology is imperative for the adoption of clean energy automotive solutions. In addition, efficient battery technology extends the useful life of the battery as well as provides improved performance to fossil fuel technology. Model predictive control (MPC) is an effective way to operate battery management systems (BMS) at their maximum capability, while maintaining the safety requirements. Using the physics-based model (PBM) of the battery allows the control system to operate on the chemical and physical process of the battery. Since these processes are internal to the battery and are physically unobservable, the extended Kalman filter (EKF) serves as a virtual observer that can monitor the physical and chemical properties that are otherwise unobservable. These three methods (i.e., PBM, EKF, and MPC) together can prolong the useful life of the battery, especially for Li-ion batteries. This capability is not limited to the automotive industry: any real-world smart application can benefit from a portable/mobile efficient BMS, compelling these systems to be executed on resource-constrained embedded devices. Furthermore, the intrinsic adaptive control process of the PBM is uniquely suited for smart systems and smart technology. However, the sheer computational complexity of PBM for MPC and EKF prevents it from being realized on highly constrained embedded devices. In this research work, we introduce a novel, unique, and efficient embedded software architecture for a PB-EKF-MPC smart sensor for BMS, specifically on embedded devices, by addressing the computational complexity of PBM. Our proposed embedded software architecture is created in such a way to be executed on a 32-bit embedded microprocessor running at 100 MHz with a limited memory of 128 KB, and still obtains an average execution time of 4.8 ms.
高效电池技术对于采用清洁能源汽车解决方案至关重要。此外，高效电池技术可延长电池的使用寿命，并提高化石燃料技术的性能。模型预测控制（MPC）是使电池管理系统（BMS）以最大能力运行同时满足安全要求的有效方法。使用基于物理的电池模型（PBM）可使控制系统基于电池的化学和物理过程运行。由于这些过程发生在电池内部且无法直接观察到，扩展卡尔曼滤波器（EKF）可作为虚拟观测器，监测那些原本不可观测的物理和化学特性。这三种方法（即PBM、EKF和MPC）共同作用可延长电池的使用寿命，尤其是对于锂离子电池。这种能力不仅限于汽车行业：任何实际的智能应用都可受益于便携式/移动高效BMS，促使这些系统在资源受限的嵌入式设备上运行。此外，PBM固有的自适应控制过程特别适用于智能系统和智能技术。然而，PBM用于MPC和EKF时极高的计算复杂度使其无法在高度受限的嵌入式设备上实现。在本研究工作中，我们通过解决PBM的计算复杂度问题，为用于BMS的PB - EKF - MPC智能传感器引入了一种新颖、独特且高效的嵌入式软件架构，具体针对嵌入式设备。我们提出的嵌入式软件架构以这样一种方式创建，即能够在运行频率为100 MHz、内存有限为128 KB的32位嵌入式微处理器上执行，并且平均执行时间仍能达到4.8毫秒。
