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        Joint estimation of state of charge and state of health of lithium‑ion battery based on fractional order model

        Yuanzhong Xu,Bohan Hu,Tiezhou Wu,Tingyi Xiao 전력전자학회 2022 JOURNAL OF POWER ELECTRONICS Vol.22 No.2

        This paper proposes a joint estimation scheme for the state of charge (SoC) and state of health (SoH) for lithium-ion batteries in electric vehicles. The estimation accuracy is improved from four aspects. First, to overcome the shortcomings of the electrochemical model and equivalent circuit model, the battery model is established by a fractional order (FO) model. Second, a genetic algorithm is used to identify the model parameters, realizing optimal parameter identification. Third, the FO adaptive extended Kalman filter-based SoC estimator is developed, and the innovation accuracy of the algorithm is improved by multi- innovation theory. Fourth, the joint estimation of SoC and SoH is formulated through a multi-timescale framework. The proposed model and method are verified through dynamic operating condition experiments, and the main results are as follows. (1) In the entire SoC range, the accuracy of the FO model is better than that of the integer order (IO) model. (2) The effectiveness of the optimized SoC estimation method is verified, and the estimation error can be controlled within 3%. (3) The effectiveness of the proposed joint estimation method in dynamic conditions is verified, and it shows high accuracy.

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        Evaluation of multi-lane transverse reduction factor under random vehicle load

        Xiaoyan Yang,Jinxin Gong,Bohan Xu,Jichao Zhu 사단법인 한국계산역학회 2017 Computers and Concrete, An International Journal Vol.19 No.6

        This paper presents the two-, three-, and four-lane transverse reduction factor based on FEA method, probability theory, and the recently actual traffic flow data. A total of 72 composite girder bridges with various spans, number of lanes, loading mode, and bridge type are analyzed with time-varying static load FEA method by ANSYS, and the probability models of vehicle load effects at arbitrary-time point are developed. Based on these probability models, in accordance to the principle of the same exceeding probability, the multi-lane transverse reduction factor of these composite girder bridges and the relationship between the multi-lane transverse reduction factor and the span of bridge are determined. Finally, the multi-lane transverse reduction factor obtained is compared with those from AASHTO LRFD, BS5400, JTG D60 or Eurocode. The results show that the vehicle load effect at arbitrary-time point follows lognormal distribution. The two-, three-, and four-lane transverse reduction factors calculated by using FEA method and probability respectively range between 0.781 and 1.027, 0.616 and 0.795, 0.468 and 0.645. Furthermore, a correlation between the FEA and AASHTO LRFD, BS5400, JTG D60 or Eurocode transverse reduction factors is made for composite girder bridges. For the two-, three-, and four-lane bridge cases, the Eurocode code underestimated the FEA transverse reduction factors by 27%, 25% and 13%, respectively. This underestimation is more pronounced in short-span bridges. The AASHTO LRFD, BS5400 and JTG D60 codes overestimated the FEA transverse reduction factors. The FEA results highlight the importance of considering span length in determining the multi-lane transverse reduction factors when designing two-lane or more composite girder bridges. This paper will assist bridge engineers in quantifying the adjustment factors used in analyzing and designing multi-lane composite girder bridges.

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