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Shen J.-N.,Shanghai JiaoTong University | He Y.-J.,Shanghai JiaoTong University | Ma Z.-F.,Shanghai JiaoTong University | Ma Z.-F.,Sinopoly Battery Research Center
AIChE Journal | Year: 2016

Equivalent circuit model (ECM) is a practical and commonly used tool not only in state of charge (SOC) estimation but also in state of health (SOH) monitoring for lithium-ion batteries (LIBs). The functional forms of circuit parameters with respect to SOC in ECM are usually empirical determined, which cannot guarantee to obtain a compact and simple model. A systematical solution framework for simultaneous functional form selection and parameter estimation is proposed. A bi-objective mixed-integer nonlinear programming (MINLP) model is first constructed. Two solution approaches, namely the explicit and implicit methods, are then developed to balance model accuracy and model complexity. The former explicitly treats the model complexity as a constraint and the latter implicitly embeds the model complexity into the objective as a penalty. Both approaches require sequential solution of the transformed MINLP model and an ideal and nadir ideal solutions-based criterion is utilized to terminate the solution procedure for determining the optimal functional forms, in which ideal solution and nadir ideal solution represent the best and worst of each objective, respectively. Both explicit and implicit approaches are thoroughly evaluated and compared through experimental pulse current discharge test and hybrid pulse power characterization test of a commercial LIB. The fitting and prediction results illustrate that the proposed methods can effectively construct an optimal ECM with minimum complexity and prescribed precision requirement. It is thus indicated that the proposed MINLP-based solution framework, which could automatically guide the optimal ECM construction procedure, can be greatly helpful to both SOC estimation and SOH monitoring for LIBs. © 2016 American Institute of Chemical Engineers. Source


Shen J.-F.,Shanghai JiaoTong University | He Y.-J.,Shanghai JiaoTong University | Ma Z.-F.,Shanghai JiaoTong University | Ma Z.-F.,Sinopoly Battery Research Center
Journal of Power Sources | Year: 2016

Accurate modeling of the charge redistribution dominated self-discharge process plays a significant role in power management systems for supercapacitors. Although equivalent circuit models (ECMs) are widely used to describe the nonlinear behaviors of the self-discharge process, they are usually separately established at different initial voltages, which might result in poor prediction performances at other unseen initial voltages. In this study, a three-branch model with a leakage resistance is used to describe the nonlinear dynamic behavior of the supercapacitor self-discharge dominated by charge redistribution and the circuit parameters in ECMs are explicitly modeled as a function of the initial voltage. Polynomial functions with different orders are systematically evaluated by means of fitting and prediction accuracy. The impacts of initial voltage and temperature on the charge redistribution dominated self-discharge process are experimentally investigated with a 3000 F commercial supercapacitor. The modeling results show that a 5th-order polynomial function is sufficient high enough to characterize the nonlinear effect of initial voltage on the charge redistribution dominated self-discharge in terms of prediction accuracy. Moreover, the prediction accuracy of polynomial function based ECMs are significantly better than that of interpolation based ECMs, which further validates the effectiveness of the proposed model. © 2015 Elsevier B.V. All rights reserved. Source


He Y.-J.,Shanghai JiaoTong University | Shen J.-N.,Shanghai JiaoTong University | Shen J.-F.,Shanghai JiaoTong University | Ma Z.-F.,Shanghai JiaoTong University | Ma Z.-F.,Sinopoly Battery Research Center
Industrial and Engineering Chemistry Research | Year: 2015

Accurate modeling of open-circuit-voltage (OCV) plays important roles both in state-of-charge (SOC) estimation and state-of-health (SOH) monitoring for lithium-ion batteries (LIBs). Monotonicity violation in OCV model would lead to inaccurate SOC estimation and ineffective of incremental capacity analysis (ICA) for SOH monitoring. In this study, first-order derivative of OCV, with respect to SOC is introduced to theoretically ensure the satisfaction of monotonicity and a nonlinear semi-infinite programming (NSIP) problem is constructed to parameter estimation. A global optimization approach via restriction of the right-hand side is used to efficiently and globally optimize the NSIP. Both fitting and ICA results demonstrate the effectiveness of the proposed method. Moreover, in comparison to the traditional polynomial and sigmoid models, the NSIP polynomial model is the best choice for performing further SOC estimation and SOH monitoring. The results thus indicate that a NSIP framework for embedding prior knowledge not only provides a promising approach to automatically capture OCV-SOC monotonicity constraint in LIBs, but also serves as a universal methodology for process modeling with the requirements of embedding derivative constraints. © 2015 American Chemical Society. Source


He Y.-J.,Shanghai JiaoTong University | Shen J.-N.,Shanghai JiaoTong University | Shen J.-F.,Shanghai JiaoTong University | Ma Z.-F.,Shanghai JiaoTong University | Ma Z.-F.,Sinopoly Battery Research Center
AIChE Journal | Year: 2015

Accurate state of health (SOH) estimation in lithium-ion batteries, which plays a significant role not only in state of charge (SOC) estimation but also in remaining useful life (RUL) prognostics is studied. SOC estimation and RUL prognostics often require one-step-ahead and long-term SOH prediction, respectively. A systematic multiscale Gaussian process regression (GPR) modeling method is proposed to tackle accurate SOH estimation problems. Wavelet analysis method is utilized to decouple global degradation, local regeneration and fluctuations in SOH time series. GPR with the inclusion of time index is utilized to fit the extracted global degradation trend, and GPR with the input of lag vector is designed to recursively predict local regeneration and fluctuations. The proposed method is validated through experimental data from lithium-ion batteries degradation test. Both one-step-ahead and multi-step-ahead SOH prediction performances are thoroughly evaluated. The satisfactory results illustrate that the proposed method outperform GPR models without trend extraction. It is thus indicated that the proposed multiscale GPR modeling method can not only be greatly helpful to both RUL prognostics and SOC estimation for lithium-ion batteries, but also provide a general promising approach to tackle complex time series prediction in health management systems. © 2015 American Institute of Chemical Engineers. Source


Huang B.-W.,Shanghai JiaoTong University | Li L.,Shanghai JiaoTong University | He Y.-J.,Shanghai JiaoTong University | Liao X.-Z.,Shanghai JiaoTong University | And 5 more authors.
Electrochimica Acta | Year: 2014

A high performance CoO/carbon nanofibers (CNF) composite catalyst was synthesized for Li-O2 batteries. For comparison, CoO/BP2000 and CoO/MWNTs were also prepared and investigated to study the influence of carbon supports on the electrochemical performance of the composite catalysts. Electrochemical tests showed that the Li-O2 battery with CoO/CNF demonstrated obviously enhanced electrochemical performance than the batteries with CoO/BP2000 and CoO/MWNTs catalysts, which delivered a first discharge capacity of 3882.5 mAh gcat -1 and remained about 3302.8 mAh gcat -1 after 8 cycles in the voltage range from 2.0 to 4.2 V. More importantly, the cycle stability of the Li-O2 battery with CoO/CNF could maintain over 50 cycles when cycled at a fixed capacity of 1000 mAh gcat -1. The unique porous nanofiberous structure of CoO/CNF greatly contributed to its high electrocatalytic performance. © 2014 Published by Elsevier Ltd. Source

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