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Fuel injection during negative valve overlap offers a promising method of controlling HCCI combustion, but sorting out the thermal and chemical effects of NVO fueling requires knowledge of temperatures throughout the cycle. Computing bulk temperatures throughout closed portions of the cycle is relatively straightforward using an equation of state, once a temperature at one crank angle is established. Unfortunately, computing charge temperatures at intake valve closing for NVO operation is complicated by a large, unknown fraction of residual gases at unknown temperature. To address the problem, we model blowdown and recompression during exhaust valve opening and closing events, allowing us to estimate in-cylinder charge temperatures based on exhaust-port measurements. This algorithm permits subsequent calculation of crank-angle-resolved bulk temperatures and residual gas fraction over a wide range of NVO operation.

The present work proposes a methodology for diesel engine development using multi-dimensional CFD and computer optimization. A multi-objective genetic algorithm coupled with the KIVA3V Release 2 code was used to optimize a high speed direct injection (HSDI) diesel engine for passenger car applications. The simulations were conducted using high-throughput computing with the CONDOR system. An automated grid generator was used for efficient mesh generation with 11 variable piston bowl geometry parameters. The first step in the procedure was to search for an optimal nozzle and piston bowl design. In this case, spray targeting, swirl ratio, and piston bowl shape were optimized separately for two full-load cases using simpler efficient combustion models (the characteristic time scale model and the shell ignition model). The optimal designs from the two optimizations were then validated using a combustion model with detailed chemistry (KIVA-CHEMKIN model and ERC n-heptane mechanism).

The developing process of steering system has experienced four phases from the simple mechanical steering system (MS), hydraulic steering system (HPS) to electric-hydraulic steering system (EHPS), till the electric power steering system (EPS) with lower energy consumption and higher performance. EPS has been found of more and more applications in the rapid development of new energy electric vehicles. This paper aims at the dynamic research of the control strategy of EPS system. Through analyzing the simplified structure model of EPS system, the mathematical models of steering wheel-steering shaft, ECU-Assist motor and rack-pinion are established by Newton's second law. The design of curve-line assist curve is one of the main research points, especially the determination method for the function expression of all the segments in the curve-line assist curve.

Hybrid, plug-in hybrid, and electric vehicles have enthusiastically embraced rechargeable Li-ion batteries as their primary/supplemental power source of choice. Because the state of charge (SoC) of a battery indicates available remaining energy, the battery management system of these vehicles must estimate the SoC accurately. To estimate the SoC of Li-ion batteries, we derive a normalized state-space model based on Li-ion electrochemistry and apply a Bayesian algorithm. The Bayesian algorithm is obtained by modifying Potter's squareroot filter and named the Potter SoC tracker (PST) in this paper. We test the PST in challenging test cases including high-rate charge/discharge cycles with outlier cell voltage measurements. The simulation results reveal that the PST can estimate the SoC with accuracy above 95% without experiencing divergence.

In order to quantitatively investigate the macroscopic characteristics of flash-boiling atomization, the spray injected through a plain-orifice nozzle under atmospheric conditions was directly imaged and analyzed by the multi-threshold algorithm. The spray images were acquired at various times after the start of actuation using a high-speed visualization system. The light intensity level of images implies the local relative mass concentration of droplets in the spray. Transient contour plots of spray images at various thresholds were analyzed and compared with turbulent round jets of diesel. A new term, transient continuous cone angle, was defined to characterize the flash-boiling spray. The relative mass concentration distributions and continuous cone angles of the sprays during the start, development and end periods of the atomization were discussed for two different sprays.

This paper studies the effect of cooled EGR on fuel consumption and anti-knock performance of a boosted port fuel injection (PFI) SI engine. Experimental results show that the cooled EGR increases the thermal efficiency by 2%∼18% depending on the operation conditions. Compared to low load operations, more improvements of the thermal efficiency are obtained at higher loads, primarily owing to the enhanced anti-knock performance, advanced combustion phasing, elimination of fuel-rich operations as well as reduced heat transfer loss with cooled EGR. The anti-knock performance of cooled EGR provides further potential to improve the thermal efficiency by increasing the compression ratio. To this end, a 1-D thermodynamic model of the engine is built and calibrated using the GT-Power code. A knock prediction correlation considering EGR is developed and validated with the experimental data.

Particle swarm optimization (PSO) is a relatively new stochastic optimization algorithm and has gained much attention in recent years because of its fast convergence speed and strong optimization ability. However, PSO suffers from premature convergence problem for quick losing of diversity. That is to say, if no particle discovers a new superiority position than its previous best location, PSO algorithm will fall into stagnation and output local optimum result. In order to improve the diversity of basic PSO, design of experiment technique is used to initialize the particle swarm in consideration of its space-filling property which guarantees covering the design space comprehensively. And the optimization procedure of PSO is divided into two stages, optimization stage and improving stage. In the optimization stage, the basic PSO initialized by Optimal Latin hypercube technique is conducted.