Abstract Three‐dimensional (3D) current collectors (CCs) have emerged as an effective strategy to inhibit dendrites and ensure the safety of lithium (Li) metal anodes. However, existing 3D CCs are generally too heavy (typically tens of mg cm −2 ) or too thick (tens to hundreds of micrometers), making large‐scale production and further application challenging. Additionally, the use of single‐component 3D CCs, whether electrically active or inert, only exhibits limited effects on stabilizing Li anodes. Here, we present a scalable screen‐printing technique for the synthesis of ultralight (~0.4 mg cm −2 ) and ultrathin (~0.54 μm) SiO 2 grids on Cu foil to regulate both the vertical electric field and Li‐ion concentration by forming an electrically active/inert dual‐function architecture. This technology breaks the limitations of traditional 3D CCs in material/fabrication costs, weight, thickness and especially, scalability for large‐scale fabrication. By using this dual‐function architecture, our Cu@SiO 2 ‐grid CCs (~8.31 mg cm −2 ), which are even lighter than the original Cu‐foil CCs (~8.85 mg cm −2 ), realize an ultra‐smooth anode surface without Li dendrites, and thus leads to an ultra‐long cyclic life of over 1500 h at 1 mA cm −2 . The assembled Li metal batteries demonstrate excellent capacity retention of ~80% over 400 cycles at 1 C and ~ 76% over 250 cycles at 5 C, which highlight the promising 3D CCs for practical applications. image
This paper presents the development of parallel direct Vlasov solvers with discontinuous Galerkin (DG) method for beam and plasma simulations in four dimensions. Both physical and velocity spaces are in two dimesions (2P2V) with unstructured mesh. Contrary to the standard particle-in-cell (PIC) approach for kinetic space plasma simulations, i.e., solving Vlasov-Maxwell equations, direct method has been used in this paper. There are several benefits to solving a Vlasov equation directly, such as avoiding noise associated with a finite number of particles and the capability to capture fine structure in the plasma. The most challanging part of a direct Vlasov solver comes from higher dimensions, as the computational cost increases as $N^{2d}$, where d is the dimension of the physical space. Recently, due to the fast development of supercomputers, the possibility has become more realistic. Many efforts have been made to solve Vlasov equations in low dimensions before; now more interest has focused on higher dimensions. Different numerical methods have been tried so far, such as the finite difference method, Fourier Spectral method, finite volume method, and spectral element method. This paper is based on our previous efforts to use the DG method. The DG method has been proven to be very successful in solving Maxwell equations, and this paper is our first effort in applying the DG method to Vlasov equations. DG has shown several advantages, such as local mass matrix, strong stability, and easy parallelization. These are particularly suitable for Vlasov equations. Domain decomposition in high dimensions has been used for parallelization; these include a highly scalable parallel two-dimensional Poisson solver. Benchmark results have been shown and simulation results will be reported.
With the development of electric drive vehicles (EDVs), the state-of-charge (SOC) estimation for lithium-ion (Li-ion) batteries has become increasingly more important. Based on the analysis of some of the most popular model-based SOC estimation methods, the proportional-integral (PI) observer is proposed to estimate the SOC of lithium-ion batteries in EDVs. The structure of the proposed PI observer is analyzed, and the convergence of the estimation method with model errors is verified. To demonstrate the superiority and compensation properties of the proposed PI observer, the simple-structure RC battery model is utilized to model the Li-ion battery. To validate the results of the proposed PI-based SOC estimation method, the experimental battery test bench is established. In the validation, the urban dynamometer driving schedule (UDDS) drive cycle is utilized, and the PI-based SOC estimation results are found to agree with the reference SOC, generally within the 2% error band for both the known and unknown initial SOC cases.
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