为了从分子水平上明确云南丘北仙女虾一物种的具体种类,采用PCR产物直接测序法测定了30个样本的COI基因与18S r DNA基因的部分序列片段(长度分别为658 bp和325 bp)。基于NCBI数据库BLAST相似性比对发现COI基因与18S r DNA基因序列同Stre...为了从分子水平上明确云南丘北仙女虾一物种的具体种类,采用PCR产物直接测序法测定了30个样本的COI基因与18S r DNA基因的部分序列片段(长度分别为658 bp和325 bp)。基于NCBI数据库BLAST相似性比对发现COI基因与18S r DNA基因序列同Streptocephalus sirindhornae相似度最高,分别为96%和100%。序列分析发现COI基因序列中共检测到15个简约信息位点,22个变异位点,8个单倍型;18S r DNA基因序列中只检测到1个简约信息位点,1个变异位点,2个单倍型。此外,结合Gen Bank中无背甲目部分科物种的同源序列,进行遗传距离和系统发育关系分析。结果显示:丘北仙女虾与弯头虫科的S.sirindhornae种间遗传距离为4.3%,属于种内水平;与钗额虫科的Thamnocephalus platyurus种间遗传距离为21.1%,属于种间水平。此外,系统发育树显示:丘北仙女虾与S.sirindhornae聚在一起形成一个单系枝,COI树的支持率为100%,18S r DNA树的支持率为72%。上述结果表明云南丘北仙女虾是弯头虫科的Streptocephalus sirindhornae,而不是钗额虫科的物种。同时,还表明COI基因相比18S r DNA基因更适合用于仙女虾物种的分子鉴定。展开更多
State of charge(SOC)estimation has always been a hot topic in the field of both power battery and new energy vehicle(electric vehicle(EV),plug-in electric vehicle(PHEV)and so on).In this work,aiming at the contradicti...State of charge(SOC)estimation has always been a hot topic in the field of both power battery and new energy vehicle(electric vehicle(EV),plug-in electric vehicle(PHEV)and so on).In this work,aiming at the contradiction problem between the exact requirements of EKF(extended Kalman filter)algorithm for the battery model and the dynamic requirements of battery mode in life cycle or a charge and discharge period,a completely data-driven SOC estimation algorithm based on EKF algorithm is proposed.The innovation of this algorithm lies in that the EKF algorithm is used to get the SOC accurate estimate of the power battery online with using the observable voltage and current data information of the power battery and without knowing the internal parameter variation of the power battery.Through the combination of data-based and model-based SOC estimation method,the new method can avoid high accumulated error of traditional data-driven SOC algorithms and high dependence on battery model of most of the existing model-based SOC estimation methods,and is more suitable for the life cycle SOC estimation of the power battery operating in a complex and ever-changing environment(such as in an EV or PHEV).A series of simulation experiments illustrate better robustness and practicability of the proposed algorithm.展开更多
文摘为了从分子水平上明确云南丘北仙女虾一物种的具体种类,采用PCR产物直接测序法测定了30个样本的COI基因与18S r DNA基因的部分序列片段(长度分别为658 bp和325 bp)。基于NCBI数据库BLAST相似性比对发现COI基因与18S r DNA基因序列同Streptocephalus sirindhornae相似度最高,分别为96%和100%。序列分析发现COI基因序列中共检测到15个简约信息位点,22个变异位点,8个单倍型;18S r DNA基因序列中只检测到1个简约信息位点,1个变异位点,2个单倍型。此外,结合Gen Bank中无背甲目部分科物种的同源序列,进行遗传距离和系统发育关系分析。结果显示:丘北仙女虾与弯头虫科的S.sirindhornae种间遗传距离为4.3%,属于种内水平;与钗额虫科的Thamnocephalus platyurus种间遗传距离为21.1%,属于种间水平。此外,系统发育树显示:丘北仙女虾与S.sirindhornae聚在一起形成一个单系枝,COI树的支持率为100%,18S r DNA树的支持率为72%。上述结果表明云南丘北仙女虾是弯头虫科的Streptocephalus sirindhornae,而不是钗额虫科的物种。同时,还表明COI基因相比18S r DNA基因更适合用于仙女虾物种的分子鉴定。
基金Projects(51607122,51378350)supported by the National Natural Science Foundation of ChinaProject(BGRIMM-KZSKL-2018-02)supported by the State Key Laboratory of Process Automation in Mining&Metallurgy/Beijing Key Laboratory of Process Automation in Mining&Metallurgy Research,China+4 种基金Project(18JCTPJC63000)supported by Tianjin Enterprise Science and Technology Commissioner Project,ChinaProject(2017KJ094,2017KJ093)supported by Tianjin Education Commission Scientific Research Plan Project,ChinaProject(17ZLZXZF00280)supported by Tianjin Science and Technology Project,ChinaProject(18JCQNJC77200)supported by Tianjin Province Science and Technology projects,ChinaProject(2017YFB1103003,2016YFB1100501)supported by National Key Research and Development Plan,China
文摘State of charge(SOC)estimation has always been a hot topic in the field of both power battery and new energy vehicle(electric vehicle(EV),plug-in electric vehicle(PHEV)and so on).In this work,aiming at the contradiction problem between the exact requirements of EKF(extended Kalman filter)algorithm for the battery model and the dynamic requirements of battery mode in life cycle or a charge and discharge period,a completely data-driven SOC estimation algorithm based on EKF algorithm is proposed.The innovation of this algorithm lies in that the EKF algorithm is used to get the SOC accurate estimate of the power battery online with using the observable voltage and current data information of the power battery and without knowing the internal parameter variation of the power battery.Through the combination of data-based and model-based SOC estimation method,the new method can avoid high accumulated error of traditional data-driven SOC algorithms and high dependence on battery model of most of the existing model-based SOC estimation methods,and is more suitable for the life cycle SOC estimation of the power battery operating in a complex and ever-changing environment(such as in an EV or PHEV).A series of simulation experiments illustrate better robustness and practicability of the proposed algorithm.
