Analysis of Optimal Deep Learning Approach for Battery Health Condition Monitoring inElectric Vehicle

,. D. N. H. A. A. A. Dr.S.Lakshmi Kanthan Bharathi, P.Sujidha
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Abstract

Compared with other commonly used batteries, lithium-ion batteries are featured by high energy density, high power density, long service life and environmental friendliness and thus have found wide application in the area of consumer electronics. The narrow area in which lithium-ion batteries operate with safety and reliability necessitates the effective control and management of battery management system. This present paper, through the analysis of literature and in combination with our practical experience, gives a brief introduction to the composition of the battery management system (BMS). First-principles models that incorporate all of the key physics that affect the internal states of a lithium-ion battery are in the form of coupled nonlinear PDEs. While these models are very accurate in terms of prediction capability, the models cannot be employed for on-line control and monitoring purposes due to the huge computational cost. A reformulated model is capable of predicting the internal states of battery with a full simulation running in milliseconds without compromising on accuracy. This paper demonstrates the feasibility of using this reformulated model for control-relevant real-time applications. The reformulated model is used to compute optimal protocols for battery operations to demonstrate that the computational cost of each optimal control calculation is low enough to be completed within the sampling interval in model predictive control (MPC). Observability studies are then presented to confirm that this model can be used for state-estimation-based MPC. A moving horizon estimator (MHE) technique was implemented due to its ability to explicitly address constraints and nonlinear dynamics. The MHE uses the reformulated model to be computationally feasible in real time. The feature of reformulated model to be solved in real time opens up the possibility of incorporating detailed physics-based model in battery management systems (BMS) to design and implement better monitoring and control strategies
电动汽车电池健康状态监测最优深度学习方法分析
与其他常用电池相比,锂离子电池具有高能量密度、高功率密度、使用寿命长、环境友好等特点,在消费电子领域得到了广泛的应用。锂离子电池安全可靠运行的小范围需要电池管理系统的有效控制和管理。本文通过对文献的分析,结合自己的实际经验,对电池管理系统(BMS)的组成进行了简要的介绍。第一原理模型以耦合非线性偏微分方程的形式包含了影响锂离子电池内部状态的所有关键物理特性。虽然这些模型在预测能力方面非常准确,但由于计算成本巨大,无法用于在线控制和监测目的。一个重新制定的模型能够预测电池的内部状态,在毫秒内进行完整的模拟,而不影响准确性。本文论证了将该模型用于控制相关实时应用的可行性。利用重新表述的模型计算电池运行的最优协议,以证明在模型预测控制(MPC)中,每个最优控制计算的计算成本足够低,可以在采样间隔内完成。然后提出了可观察性研究,以证实该模型可用于基于状态估计的MPC。移动视界估计(MHE)技术由于能够明确地处理约束和非线性动力学问题而得以实现。MHE采用新建立的模型,使其在计算上实时可行。重新制定的模型可实时解决的特点,为在电池管理系统(BMS)中纳入详细的基于物理的模型以设计和实施更好的监测和控制策略提供了可能性
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