Noise-Immune Model Identification and State-of-Charge Estimation for Lithium-Ion Battery Using Bilinear Parameterization

Accurate estimation of state of charge (SOC) is critical to the safe and efficient utilization of a battery system. Model-based SOC observers have been widely used due to their high accuracy and robustness, but they rely on a well-parameterized battery model. This article scrutinizes the effect of measurement noises on model parameter identification and SOC estimation. A novel parameterization method combining instrumental variable (IV) estimation and bilinear principle is proposed to compensate for the noise-induced biases of model identification. Specifically, the IV estimator is used to reformulate an overdetermined system so as to allow coestimating the model parameters and noise variances. The coestimation problem is then decoupled into two linear subproblems which are solved efficiently by a two-stage least squares algorithm in a recursive manner. The parameterization method is further combined with a Luenberger observer to estimate the SOC in real time. Simulations and experiments are performed to validate the proposed method. Results reveal that the proposed method is superior to existing method in terms of the immunity to noise corruption

Deep Deterministic Policy Gradient-DRL Enabled Multiphysics-Constrained Fast Charging of Lithium-Ion Battery

Fast charging is an enabling technique for the large-scale penetration of electric vehicles. This paper proposes a knowledge-based, multi-physics-constrained fast charging strategy for lithium-ion battery (LIB), with a consciousness of the thermal safety and degradation. A universal algorithmic framework combining model-based state observer and a deep reinforcement learning (DRL)-based optimizer is proposed, for the first time, to provide a LIB fast charging solution. Within the DRL framework, a multi-objective optimization problem is formulated by penalizing the over-temperature and degradation. An improved environmental perceptive deep deterministic policy gradient (DDPG) algorithm with priority experience replay is exploited to trade-off smartly the charging rapidity and the compliance of physical constraints. The proposed DDPG-DRL strategy is compared experimentally with the rule-based strategies and the state-of-the-art model predictive controller to validate its superiority in terms of charging rapidity, enforcement of LIB thermal safety and life extension, as well as the computational tractability

Signal-disturbance interfacing elimination for unbiased model parameter identification of lithium-ion battery

A precisely parameterized battery model is the prerequisite of the model-based management of lithium-ion battery. However, the unexpected sensing of noises may discount the identification of model parameters in practical applications. This article focuses on the noise effect compensation and online parameter identification for the widely used equivalent circuit model. A novel degree of freedom (DOF) eliminator is proposed and combined with the Frisch scheme in a recursive fashion, for the first time, to coestimate the noise statistics and unbiased model parameters. A computationally tractable numerical solver is further proposed for the DOF eliminator to improve the real-time performance. Simulations and experiments are performed to validate the proposed method from theoretical to practical perspective. Results show that the proposed method can effectively mitigate the noise-induced identification biases and outperform the existing methods in terms of the accuracy and the robustness to noise corruption.This work was supported by the National Key R&D Program of China under Grant 2017YFB0103802

Climate Drivers of Pine Shoot Beetle Outbreak Dynamics in Southwest China

Outbreaks of pine shoot beetles (Tomicus spp.) have caused widespread tree mortality in Southwest China. However, the understanding of the role of climatic drivers in pine shoot beetle outbreaks is limited. This study aimed to characterize the relationships between climate variables and pine shoot beetle outbreaks in the forests of Yunnan pine (Pinus yunnanensis Franch) in Southwest China. The pine shoot beetle-infested total area from 2000 to 2017 was extracted from multi-data Landsat images and obtained from field survey plots. A temporal prediction model was developed by partial least squares regression. The results indicated that multi consecutive year droughts was the strongest predictor, as such a condition greatly reduced the tree resistance to the beetles. The beetle-infested total area increased with spring temperature, associated with a higher success rate of trunk colonization and accelerated larval development. Warmer temperatures and longer solar radiation duration promoted flight activity during the trunk transfer to the shoot period and allowed the completion of sister broods. Multi consecutive year droughts combined with the warmer temperatures and higher solar radiation duration could provide favorable conditions for shoot beetle outbreaks. Generally, identifying the climate variables that drive pine shoot beetle outbreaks could help improve current strategies for outbreak control

Climate Drivers of Pine Shoot Beetle Outbreak Dynamics in Southwest China

Outbreaks of pine shoot beetles (Tomicus spp.) have caused widespread tree mortality in Southwest China. However, the understanding of the role of climatic drivers in pine shoot beetle outbreaks is limited. This study aimed to characterize the relationships between climate variables and pine shoot beetle outbreaks in the forests of Yunnan pine (Pinus yunnanensis Franch) in Southwest China. The pine shoot beetle-infested total area from 2000 to 2017 was extracted from multi-data Landsat images and obtained from field survey plots. A temporal prediction model was developed by partial least squares regression. The results indicated that multi consecutive year droughts was the strongest predictor, as such a condition greatly reduced the tree resistance to the beetles. The beetle-infested total area increased with spring temperature, associated with a higher success rate of trunk colonization and accelerated larval development. Warmer temperatures and longer solar radiation duration promoted flight activity during the trunk transfer to the shoot period and allowed the completion of sister broods. Multi consecutive year droughts combined with the warmer temperatures and higher solar radiation duration could provide favorable conditions for shoot beetle outbreaks. Generally, identifying the climate variables that drive pine shoot beetle outbreaks could help improve current strategies for outbreak control