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Ash accumulation in the channels of ceramic, honeycomb-type particulate filters is controlled by several key parameters, which are the focus of this study. Ultimately, it is the formation of ash deposits, their transport, and the manner in which the ash accumulates in the particulate filter, which determines the useful service life of the filter and its resulting impact on engine performance. Although significant variations in ash deposit properties and their spatial distribution within the filter channels have been reported, depending on the filter's application, understanding the key parameters and mechanisms, such as the effects of exhaust flow and temperature conditions, as well as the processes occurring during filter regeneration events (whether passive or active) are critical in developing improved filter ash management strategies.

Ash inside a honeycomb-configured diesel particulate filter (DPF) inlet channel accumulates both as a cake layer along the channel walls and as a “plug” towards the back of the channel. Experimental studies of DPF ash distribution have shown both an axial variation of deposits along channels and accumulation towards the end plugs. This study evaluates the sensitivity of DPF pressure drop on ash axial distribution and the potential to reduce flow restrictions by controlling and optimizing the spatial distribution of ash inside DPF channels. A computational model has been used in conjunction with experimental data to illustrate the sensitivity of ash spatial distribution on DPF performance. The classical constant-thickness DPF one-dimensional models have substantially been updated to include layer thickness axial variations. Material properties, such as ash characteristics, are provided by recent experiments at the authors' laboratory.

“Soichiro Honda Cup, Honda Econo-Power Competition”, is an annual international energy-saving competition which is hosted by Honda Motor Co., Ltd. Till now it has been held 27 sessions. The aims of the EP project are: promoting the development of environmental protection, making full use of limit earth resources, challenging the fuel consumption limitation of vehicle. Tongji University's students' team has participated in the competition for seven consecutive times. In order to minimize the fuel consumption of the EP energy-saving vehicle, this paper focuses on the technical methods of improving the fuel economy of the engine. Firstly, the optimization of the carburetor. Secondly, for the purpose of improving combustion efficiency, researches on dual spark plug and compression ratio are done.

The accumulation of soot and lubrication-derived ash particles in a diesel particulate filter (DPF) increases exhaust flow restriction and negatively impacts engine efficiency. Previous studies have described the macroscopic phenomenon and general effects of soot and ash accumulation on filter pressure drop. In order to enhance the fundamental understanding, this study utilized a novel apparatus that of a dual beam scanning electron microscope (SEM) and focused ion beam (FIB), to investigate microscopic details of soot and ash accumulation in the DPF. Specifically, FIB provides a minimally invasive technique to analyze the interactions between the soot, ash, catalyst/washcoat, and DPF substrate with a high degree of measurement resolution. The FIB utilizes a gallium liquid metal ion source which produces Ga+ ions of sufficient momentum to directionally mill away material from the soot, ash, and substrate layers on a nm-μm scale.

In the Plug-in hybrid electric bus, the power train parameter and control strategy significantly affect the economy and dynamic performance. Thus, the simultaneous optimization of power train parameter and control strategy is designed for the trade-off between the dynamic and economic performance. Depending on the parallel electric auxiliary control strategy in a plug-in hybrid electric bus, a vehicle dynamic simulation model is built with the software AVL Cruise. Aiming at the minimization of equivalent gas consumption and acceleration time from 0 to 50kmph, the gear ratio, final drive ratio, gear shifting strategy and control strategy are chosen as optimal variable, which significantly impact power performance and fuel economy. The driving performance and the driving range with full battery are considered as constraints. Based on the software Isight, multi-objective optimization model is built by adopting non-dominated sorting genetic optimization algorithm (NSGA-II).

A two-dimensional mesoscopic approach has been developed to investigate the flow and soot loading in the micro-channels of diesel particulate filter. Soot particle size examined is in the range of 10 nm to 10 μm. The flow is solved by an incompressible lattice Boltzmann model and the transport of solid particle is described in a Lagrangian frame of reference by cell automation probabilistic model. The lattice Boltzmann-cell automation probabilistic model (LB-CA model) is validated with the results of previous studies. The heterogeneous porous wall of DPF is generated by quartet structure generation set (QSGS). The effects of porous wall on the pressure field and velocity field are investigated. The distribution and deposition of soot particles with different sizes in clean channels are simulated. The dynamic evolution of solid boundary in soot particle capture process is investigated and the effects of the deposited soot particles on flow field are evaluated.

Current CJ-4 lubricant specifications place chemical limits on diesel engine oil formulations to minimize the accumulation of lubricant-derived ash in diesel particulate filters (DPF). While lubricant additive chemistry plays a strong role in determining the amount and type of ash accumulated in the DPF, a number of additional factors play important roles as well. Relative to soot particles, whose residence time in the DPF is short-lived, ash particles remain in the filter for a significant fraction of the filter's useful life. While it is well-known that the properties (packing density, porosity, permeability) of soot deposits are primarily controlled by the local exhaust conditions at the time of particle deposition in the DPF, the cumulative operating history of the filter plays a much stronger role in controlling the properties and distribution of the accumulated ash.

It is commonly believed that running-in behavior is related to engine reliability and fuel economy. This paper uses a methodology to find the influence of running-in, based on telematics data. In this paper, the key related telematics parameters are identified to assess running-in behaviors through feature analytics with telematics vehicle real-road data. By analyzing these parameters, truck groups subjected to different running-in behaviors are classified to evaluate the relationship between running-in behaviors and fuel economy.

Considering the defect of current test method and learning from the developing experience of other methods around the world, a method adopted China Light-duty Vehicle Test Cycle (CLTC) which is suitable for the real condition of Chinese road was put forword. Through comparing the test results of 20 vehicles with big data statistics, the results obtained by this method are close to those from customers and the difference between them is around 6%. Thus, the rationality of this method is proved. Furthermore, this method can evaluate the real fuel consumption of vehicle running on Chinese roads.

Radio frequency (RF)-based sensors provide a direct measure of the particulate filter loading state. In contrast to particulate matter (PM) sensors, which monitor the concentration of PM in the exhaust gas stream for on-board diagnostics purposes, RF sensors have historically been applied to monitor and control the particulate filter regeneration process. This work developed an RF-based particulate filter control system utilizing both conventional and fast response RF sensors, and evaluated the feasibility of applying fast-response RF sensors to provide a real-time measurement of engine-out PM emissions. Testing with a light-duty diesel engine equipped with fast response RF sensors investigated the potential to utilize the particulate filter itself as an engine-out soot sensor.

Diesel Particulate Filters (DPF) are a key component in many on- and off-road aftertreatment systems to meet increasingly stringent particle emissions limits. Efficient thermal management and regeneration control is critical for reliable and cost-effective operation of the combined engine and aftertreatment system. Conventional DPF control systems predominantly rely on a combination of filter pressure drop measurements and predictive models to indirectly estimate the soot loading state of the filter. Over time, the build-up of incombustible ash, primarily derived from metal-containing lubricant additives, accumulates in the filter to levels far exceeding the DPF's soot storage limit. The combined effects of soot and ash build-up dynamically impact the filter's pressure drop response, service life, and fuel consumption, and must be accurately accounted for in order to optimize engine and aftertreatment system performance.

Efficient aftertreatment management requires accurate sensing of both particulate filter soot and ash levels for optimized feedback control. Currently a combination of pressure drop measurements and predictive models are used to indirectly estimate the loading state of the filter. Accurate determination of filter soot loading levels is challenging under certain operating conditions, particularly following partial regeneration events and at low flow rate (idle) conditions. This work applied radio frequency (RF)-based sensors to provide a direct measure of the particulate filter soot levels in situ. Direct measurements of the filter loading state enable advanced feedback controls to optimize the combined engine and aftertreatment system for improved DPF management. This study instrumented several cordierite and aluminum titanate diesel particulate filters with RF sensors. The systems were tested on a range of light- and heavy-duty applications, which included on- and off-road engines.

Dynamic and economic performance play an important role in the vehicle evaluation indexes, and also are crucial parts considered during the design progress. This paper applies a method of parameters matching of powertrain to one Pure Electric City Bus(PECB), with which a four-speed transmission dynamic system is designed. Meanwhile two powertrain models adopting motors with identical peak power but different base speeds and peak torques coupled with two-speed transmission or without transmission are introduced for comparison to analyze if a smaller motor performs better in performance. The three models are based on identical type of bus. Then according to the powertrain parameters, simulation models of three bus models are established respectively. From the results of simulation, vehicle performances of the four-speed transmission powertrain, especially economic performance, are compared with the other two.

A pressure accumulative injection rate controllable unit injector-PAIRCUI is proposed and developed. This unit injector is powered by fuel pressure accumulation controlled by an electronic control unit and its injection rate is shaped by inner valves of the injector. Inherent advantages of an accumulator type unit injector have been carried out in this new design, including sructural simplicity, totally flexible injection timing, medium common rail pressure, tolerable pump size and flow requirement. A number of decisive features have also been realized that are significant for high efficiency and low emissions of engine combustion, including higher mean effective injection pressure(MEIP), pilot injection capability and rapid end of injection. The injection pressure is independent of engine speed, but regulated upon engine load. These characteristics are beneficial in improving engine performance and fuel consumption.

The vibration isolation performance of vehicle powertrain mounting system is mostly determined by the three-directional stiffness of each mount block. Because of the manufacturing tolerance and the coupling effect, the stiffness of mounts cannot be maintained stable. The purpose of this study was to find out the way to optimize the stiffness of mounts via the design of experiments (DOE). According to the DOE process, a full factorial design was implemented. The z-direction stiffness of three mount blocks in the mounting system was selected as the three analysis factors. The maximum and the minimum stiffness of each mount block within the manufacturing tolerance were selected as the two levels. The measured vibration of vehicle body under certain loading case was selected as the response factor. After eight times of experiment, the DOE parameters were analyzed with statistical methods.

Accurate and online capacity estimation is of extreme importance to maintain the continuous operation of lithium-ion batteries. This paper proposes an indirect capacity estimation method based on the incremental capacity features and model interpretability. First, the current and voltage data of the battery are collected in real-time to construct the incremental capacity curves. The dual filter, which consists of a moving average and a Gaussian filter, is then used to smooth the curves. To achieve satisfying filtering effects, the filter window size and calculation frequency are determined by comparing different sets of conditions. 15 multiple alternative features related to the curve peak position and area are extracted. The Shapley value method based on cooperative game theory is introduced to reduce the dimension of the feature vector and to determine the key features.

The increasing use of diesel and gasoline particulate filters requires advanced on-board diagnostics (OBD) to prevent and detect filter failures and malfunctions. Early detection of upstream (engine-out) malfunctions is paramount to preventing irreversible damage to downstream aftertreatment system components. Such early detection can mitigate the failure of the particulate filter resulting in the escape of emissions exceeding permissible limits and extend the component life. However, despite best efforts at early detection and filter failure prevention, the OBD system must also be able to detect filter failures when they occur. In this study, radio frequency (RF) sensors were used to directly monitor the particulate filter state of health for both gasoline particulate filter (GPF) and diesel particulate filter (DPF) applications.

Regarding safety, obstacle avoidance has been considered as one of the most important features among ADAS systems for ground vehicles. However, the implementation of obstacle avoidance functions to commercial vehicles are still under progress. In this paper, we demonstrate a complete process of obstacle avoidance strategy for unmanned ground vehicle and implement the strategy on the self-developed Arduino based RC Car. In this process, the sensor LIDAR was used to detect the obstacles on the fore-path. Based on the measured LIDAR data, an optimized path is automatically generated with accommodation of current car position, obstacle locations, car operation capability and global environmental restrictions. The path planning is updated in real time while new or changing obstacles being detected. This algorithm is validated by the simulation results with the RC car. The comparison will be discussed at the end of this paper.

Sintered metal fiber (SMF) diesel particulate filters (DPF) has more than one order of magnitude lower pressure drop compared to a granular or reaction-born DPF of the same (clean) filtration efficiency. To better understand the filtration process and optimize the filter performance, metal fiber filter models are developed in this study. The major previous theoretical models for clean fibrous filter are summarized and compared with experimental data. Furthermore, a metal fiber DPF soot loading model, using similar concept developed in high efficiency particulate air (HEPA) filter modeling, is built to simulate filter soot loading performance. Compared with experimental results, the soot loading model has relatively good predictions of filter pressure drop and filtration efficiency.

The increasing use of gasoline direct injection (GDI) engines coupled with the implementation of new particulate matter (PM) and particle number (PN) emissions regulations requires new emissions control strategies. Gasoline particulate filters (GPFs) present one approach to reduce particle emissions. Although primarily composed of combustible material which may be removed through oxidation, particle also contains incombustible components or ash. Over the service life of the filter the accumulation of ash causes an increase in exhaust backpressure, and limits the useful life of the GPF. This study utilized an accelerated aging system to generate elevated ash levels by injecting lubricant oil with the gasoline fuel into a burner system. GPFs were aged to a series of levels representing filter life up to 150,000 miles (240,000 km). The impact of ash on the filter pressure drop and on its sensitivity to soot accumulation was investigated at specific ash levels.