An Optimized FPGA-Based Hardware Accelerator for Physics-Based EKF for Battery Cell Management

机译：基于FPGA的基于EKF的优化FPGA硬件加速器，用于电池管理

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Battery technology is the cornerstone of HEVs. Model Predictive Control coupled with physics-based model (PBM) is an effective technique for battery management systems (BMS). In this case, extended Kalman filter (EKF) is used as the state observer for the highly non-linear PBM. Thus far, the sheer computational complexity of PBM hinders it from being utilized for portable BMS. In this paper, we introduce a novel and efficient FPGA-based hardware accelerator for physics-based EKF, to address the computational complexity of PBM.

机译：电池技术是混合动力汽车的基石。模型预测控制与基于物理的模型（PBM）结合使用是电池管理系统（BMS）的有效技术。在这种情况下，扩展卡尔曼滤波器（EKF）用作高度非线性PBM的状态观察器。到目前为止，PBM的纯粹的计算复杂性阻碍了它被用于便携式BMS。在本文中，我们针对基于物理的EKF引入了一种新颖且高效的基于FPGA的硬件加速器，以解决PBM的计算复杂性。

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《IEEE International Symposium on Circuits and Systems》|2020年|1-5|共5页
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Anne K. Madsen; M. Scott Trimboli; Darshika G. Perera;
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Computer architecture; Hardware; Mathematical model; Batteries; Computational modeling; Field programmable gate arrays; Kalman filters;

机译：计算机体系结构;硬件;数学模型;电池;计算模型;现场可编程门阵列;卡尔曼滤波器;

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机译：基于多目标粒子群算法的混合动力管理/燃料电池/电池插电式混合动力汽车优化设计
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机译：采用遗传算法的现代电动车电池电池热管理优化
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机译：电动车辆电池电池管理系统模型预测控制算法优化的嵌入式架构
6. Families of FPGA-Based Accelerators for Approximate String Matching [O] . Tom Van Court, Martin C. Herbordt -1

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An Optimized FPGA-Based Hardware Accelerator for Physics-Based EKF for Battery Cell Management

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