Abstract: Two types of commercial cylindrical lithium ion
batteries (Panasonic 3.4 Ah NCR-18650B and Samsung 2.9 Ah
INR-18650), were investigated experimentally. The capacities of these
samples were individually measured using constant current-constant
voltage (CC-CV) method at different ambient temperatures (-10°C,
0°C, 25°C). Their internal resistance was determined by
electrochemical impedance spectroscopy (EIS) and pulse discharge
methods. The cells with different configurations of parallel connection
NCR-NCR, INR-INR and NCR-INR were charged/discharged at the
aforementioned ambient temperatures. The results showed that the
difference of internal resistance between cells much more evident at
low temperatures. Furthermore, the parallel connection of NCR-NCR
exhibits the most uniform temperature distribution in cells at -10°C,
this feature is quite favorable for the safety of the battery pack.
Abstract: To solve saddle point systems efficiently, several preconditioners have been published. There are many methods for constructing preconditioners for linear systems from saddle point problems, for instance, the relaxed dimensional factorization (RDF) preconditioner and the augmented Lagrangian (AL) preconditioner are used for both steady and unsteady Navier-Stokes equations. In this paper we compare the RDF preconditioner with the modified AL (MAL) preconditioner to show which is more effective to solve Navier-Stokes equations. Numerical experiments indicate that the MAL preconditioner is more efficient and robust, especially, for moderate viscosities and stretched grids in steady problems. For unsteady cases, the convergence rate of the RDF preconditioner is slightly faster than the MAL perconditioner in some circumstances, but the parameter of the RDF preconditioner is more sensitive than the MAL preconditioner. Moreover the convergence rate of the MAL preconditioner is still quite acceptable. Therefore we conclude that the MAL preconditioner is more competitive than the RDF preconditioner. These experiments are implemented with IFISS package.
Abstract: A novel path planning approach is presented to solve
optimal path in stochastic, time-varying networks under priori traffic
information. Most existing studies make use of dynamic programming
to find optimal path. However, those methods are proved to
be unable to obtain global optimal value, moreover, how to design
efficient algorithms is also another challenge.
This paper employs a decision theoretic framework for defining
optimal path: for a given source S and destination D in urban transit
network, we seek an S - D path of lowest expected travel time
where its link travel times are discrete random variables. To solve
deficiency caused by the methods of dynamic programming, such as
curse of dimensionality and violation of optimal principle, an integer
programming model is built to realize assignment of discrete travel
time variables to arcs. Simultaneously, pruning techniques are also
applied to reduce computation complexity in the algorithm. The final
experiments show the feasibility of the novel approach.