Achieving accurate and reliable remaining useful life (RUL) prediction of lithium-ion batteries is very vital for normal operation of battery system. The direct RUL prediction based on capacity largely depends on laboratory condition. A novel method which combines indirect health indicator (HI) and multiple Gaussian process regression (GPR) model is presented for RUL forecast to solve the capacity unmeasurable problem of operating battery in this paper. Firstly, three measurable HIs are extracted in constant current and constant voltage charge process. Both Pearson and Spearman rank correlation analytical approaches show that the correlations between the HIs and the capacity are good. Then, the GPR model is optimized with combined kernel functions to improve the ability of predicting the capacity regeneration. Next, based on the measurable HI versus cycle number data, three GPR models are built and the HIs prognosis results are achieved at single point. The HIs prediction results are added in the multidimensional GPR model which is accomplished by using the HIs and capacity as input and output, respectively. The predicted capacity is used to compare with the threshold to acquire RUL prediction result. The approach is validated by the two different life-cycle test datasets. The results indicate that an accurate and reliable RUL forecast of lithium-ion batteries can be realized by using the proposed approach.

This paper presents an energy management strategy (EMS) design and optimization approach for a plug-in hybrid electric vehicle (PHEV) with a hybrid energy storage system (HESS) which contains a Li-Ti-O battery pack and a Ni-Co-Mn battery pack. The EMS shares power flows within the hybrid powertrain, and it employs a dual fuzzy logical controller (DFLC) whose inputs are predictions for PHEV powertrain states. An elitist non-dominant genetic algorithm using a model in loop simulation approach as fitness functions is implemented to multi-objective optimization for the EMS under Worldwide Light-duty Test Cycles. The optimal objectives are improving PHEV mileage, minimizing battery packs capacity fades, reducing HESS degradation inconsistency and minimizing driving cost unit distance. A hardware in loop test bench has been established to verify EMS performances in embedded systems. The test results under New European Driving Cycles demonstrate that optimized EMSs remain appropriate for different driving cycles and their performances are close to dynamic programming based offline optimal solutions. Due to the contributions of both the HESS and the optimized EMS, the PHEV energy efficiency has been improved by 1.6~2.5% and the PHEV energy storage system cycle life can be improved by 159%~203%.

For guaranteeing the performance and safety of battery systems, a valid fault diagnostic method is quite essential. This paper presents a systematic fault diagnostic scheme based on hybrid system for the typical faults of lithium-ion battery packs, including sensor faults and relay faults. The automata are established based on hybrid system theory to simultaneously capture the continuous dynamics and discrete dynamics of the battery pack. The distributed diagnostic structure is adopted to avoid constructing the global model of the battery pack so that the computation burden can be reduced. Dual extended Kalman filter algorithm is developed to estimate the parameters, terminal voltages and state of charges of each cell in the battery pack. The current, terminal voltage residual and state of charge residual are used to implement the distinguishability analysis. The diagnostic scheme is implemented based on both the observation of the events and the distinguishability analysis of the continuous dynamics. The performance of the diagnostic method is validated for the battery pack which contains 2 batteries in series connection and 2 branches in parallel connection through the Federal Urban Driving Schedule driving cycle.

The subcellular localization of proteins is a fundamental aspect of protein functions. Determining the subcellular localization is important for understanding the biological functions of proteins. Here, we developed a set of rice organelle marker lines, in which the expressing fluorescent organelle markers could be used as comparative standards in determining the subcellular localization of the protein of interest. We constructed green fluorescent protein (GFP)- and/or Discosoma sp. red fluorescent protein (DsRed)-tagged organelle markers targeted to the endoplasmic reticulum (ER), mitochondria, Golgi apparatus, peroxisome, actin cytoskeleton, plastid, tonoplast, plasma membrane, and nucleus, respectively. The utility of the rice marker lines for protein subcellular localization studies was demonstrated by detecting a nucleus-localized OsWRKY45 and a mitochondria-associated NbHxk1 in protoplasts of the GFP-OsH2B and the ScCOX4-DsRed lines, respectively. Using a sheath-inoculation method, followed by a live-cell imaging, we detected co-localization of a Magnaporthe oryzae PWL2:mCherry : NLS fusion with the nucleus marker in the GFP-OsH2B rice epidermal cells, confirming the translocation of the M. oryzae effector PWL2 into host cells, and further demonstrating the feasibility of using the organelle marker lines for studying dynamics of proteins in rice cells in the interactions between rice and pathogens. The set of organelle marker lines developed in the present study, provides a valuable resource for protein subcellular localization studies in rice.