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Low-pressure cooled EGR (LP-cEGR) systems can provide significant improvements in spark-ignition engine efficiency and knock resistance. However, open-loop control of these systems is challenging due to low pressure differentials and the presence of pulsating flow at the EGR valve. This research describes a control structure for Low-pressure cooled EGR systems using closed loop feedback control along with internal model control. A Smith Predictor based PID controller is utilized in combination with an intake oxygen sensor for feedback control of EGR fraction. Gas transport delays are considered as dead-time delays and a Smith Predictor is one of the conventional methods to address stability concerns of such systems. However, this approach requires a plant model of the air-path from the EGR valve to the sensor.

Diesel engine exhaust poses an ongoing threat to human health as well as to the environment. Automotive exhaust treatment systems have been developed over the years to reduce the large amount of diesel particulate matter (DPM) released to the atmosphere. Current systems can be categorized as selective catalytic reduction, catalytic converters, and diesel particulate filters. This study presents an emission system that focuses on the removal of exhaust particles using Brownian diffusion of DPM toward fog drops followed by cyclonic separation of DPM rich fog drops. The experimental system consisted of a 13.2 kW diesel engine, heat exchanger to cool the exhaust to saturation temperature, ultrasonic fogger, cyclone separator, and recovery of waste particulate. Representative emission tests have been performed at five different diesel engine speeds and corresponding crankshaft loads.

Most studies on supervisory controllers of hybrid electric vehicles consider only fuel economy in the objective function. Taking into consideration the importance of the energy storage system health and its impact on the vehicle’s functionality, cost, and warranty, recent studies have included battery degradation as the second objective function by proposing different energy management strategies and battery life estimation methods. In this paper, a rule-based supervisory controller is proposed that splits the torque demand based not only on fuel consumption, but also on the battery capacity fade using the concept of severity factor. For this aim, the severity factor is calculated at each time step of a driving cycle using a look-up table with three different inputs including c-rate, working temperature, and state of charge of the battery. The capacity loss of the battery is then calculated using a semi-empirical capacity fade model.

Taking full advantage of available traffic environment information, making control decisions, and then planning trajectory systematically under structured roads conditions is a critical part of intelligent vehicle. In this article, a lane-changing decision-making method for intelligent vehicle is proposed based on acceleration field. Firstly, an acceleration field related to relative velocity and relative distance was built based on the analysis of braking process, and acceleration was taken as an indicator of safety evaluation. Then, a lane-changing decision method was set up with acceleration field while considering driver’s habits, traffic efficiency and safety. Furthermore, velocity regulation was also introduced in the lane-changing decision method to make it more flexible.

With recent advances in electric vehicles, there is a plethora of powertrain topologies and components available in the market. Thus, the performance of electric vehicles is highly sensitive to the choice of various powertrain components. This paper presents a multi-objective optimization model that can optimally select component sizes for batteries, supercapacitors, and motors in regular passenger battery-electric vehicles (BEVs). The BEV topology presented here is a hybrid BEV which consists of both a battery pack and a supercapacitor bank. Focus is placed on optimal selection of the battery pack, motor, and supercapacitor combination, from a set of commercially available options, that minimizes the capital cost of the selected power components, the fuel cost over the vehicle lifespan, and the 0-60 mph acceleration time. Available batteries, supercapacitors, and motors are from a market survey.

A quasi-dimensional multi-zone model for diesel spray combustion has been developed. The model contains most of the physical processes of diesel spray combustion, and is simplified and economical. The zone formation is based on the fuel injection parameters. For the wall jet penetration velocity, a new equation is used based on the effect of the impinging free jet on the wall jet. For the fuel evaporation, an approximate solution of the instantaneous variations of droplet diameter is given in the simple algebraic equations based on the individual effect of the evaporation and the heat transfer from ambient gas. The soot emission sub-model calculates the soot concentration. This model has been applied for a direct injection diesel engine. The calculated results have shown a reasonable agreement with the experimental results. A parametric study has been carried out.

This research proposes a control system for Spark Ignition (SI) engines with external Exhaust Gas Recirculation (EGR) based on model predictive control and a disturbance observer. The proposed Economic Nonlinear Model Predictive Controller (E-NMPC) tries to minimize fuel consumption for a number of engine cycles into the future given an Indicated Mean Effective Pressure (IMEP) tracking reference and abnormal combustion constraints like knock and combustion variability. A nonlinear optimization problem is formulated and solved in real time using Sequential Quadratic Programming (SQP) to obtain the desired control actuator set-points. An Extended Kalman Filter (EKF) based observer is applied to estimate engine states, combining both air path and cylinder dynamics. The EKF engine state(s) observer is augmented with disturbance estimation to account for modeling errors and/or sensor/actuator offset.

Electrified transportation has received significant interest recently because of sustainable and clean energy goals. However, the degradation of electrical components such as energy storage systems raises system reliability and economic concerns. In this paper, a prognostic-based control strategy is proposed for hybrid electric vehicles (HEVs) to abate the degradation of energy systems. Degradation forecasting models of electrical components are developed to predict their degradation paths. The predicted results are then used to control HEVs in order to reduce the degradation of components.

This paper proposes a novel reconfigurable battery balancing topology and reinforcement learning-based intelligent balancing management system. The different degradations cause a significant loss of battery pack available capacity, as the pack power output relies on the weakest cell due to the relevant physical requirements. To handle this capacity drop issue, a reconfigurable battery topology is adopted to improve the usability of the heterogeneous battery. There are some existing battery reconfigurable topologies in the literature. However, these studies rely on the limited options of topology designs, and there is a lack of study on the reconfigurability of these designs and other possible new designs. Also, it is rare to find an optimal management system for the reconfigurable battery topology. To fill these research gaps, this paper explores existing battery reconfigurable topology designs and proposes a new reconfigurable topology for battery balancing.

This paper seeks to identify key input parameters needed to achieve a production-viable control strategy for spark-assisted compression ignition (SACI) engines. SACI is a combustion strategy that uses a spark plug to initiate a deflagration flame that generates sufficient ignition energy to trigger autoignition in the remaining charge. The flame propagation phase limits the rate of cylinder pressure rise, while autoignition rapidly completes combustion. High dilution within the autoignited charge is generally required to maintain reaction rates feasible for production. However, this high dilution may not be reliably ignited by the spark plug. These competing constraints demand novel mixture preparation strategies for SACI to be feasible in production. SACI with charge stratification has demonstrated sufficiently stable flame propagation to reliably trigger autoignition across much of the engine operating map.

Accurate in-cylinder air charge estimation is important for engine torque determination, controlling air-to-fuel ratio, and ensuring high after-treatment efficiency. Spark ignition (SI) engine technologies like variable valve timing (VVT) and exhaust gas recirculation (EGR) are applied to improve fuel economy and reduce pollutant emissions, but they increase the complexity of air charge estimation. Increased air-path complexity drives the need for cost effective solutions that produce high air mass prediction accuracy while minimizing sensor cost, computational effort, and calibration time. A large number of air charge estimation techniques have been developed using a range of sensors sets combined with empirical and/or physics-based models. This paper provides a technical review of research in this area, focused on SI engines.

Part V of this five-part paper investigates the flow field and the total pressure loss mechanisms for three valve lifts in the exhaust region of a V8 racecar engine using the robust, systematic computational methodology described in Part I. The replica of the engine geometry includes a cylinder, detailed combustion chamber, exhaust valve, valve seat, port, and “exhaust pipe”. A set of fully-converged and grid-independent solutions for the steady, time-averaged (or RANS), non-linear Navier-Stokes equations are obtained using dense and high quality grids, involving 2.1∼3.0 finite volumes, and unusually strict convergence criteria. Turbulence closure is attained via the realizable k-ε (RKE) model used in conjunction with the non-equilibrium wall function near-wall treatment. The validation presented in Part I showed that flow rate results from the “blind simulations” agree well with the experimental measurements.

This paper presents a study of LPG lean burn in a motorcycle SI engine. The lean burn limits are compared by several ways. The relations of lean burn limit with the parameters, such as engine speed, compression ratio and advanced spark ignition etc. are tested. The experimental results show that larger throttle opening, lower engine speed, earlier spark ignition timing, larger electrode gap and higher compression ratio will extend the lean burn limit of LPG. The emission of a LPG engine, especially on NOx emission, can be significantly reduced by means of the lean burn technology.

Internal combustion engines, as the dominant power source in the transportation sector and the primary contributor to carbon emissions, face both significant challenges and opportunities in the context of achieving carbon neutral goal. Biofuels, such as biodiesel produced from biomass, and zero-carbon fuel ammonia, can serve as alternative fuels for achieving cleaner combustion in internal combustion engines. The dual-fuel combustion of ammonia-biodiesel not only effectively reduces carbon emissions but also exhibits promising combustion performance, offering a favorable avenue for future applications. However, challenges arise in the form of unburned ammonia (NH3) and N2O emissions. This study, based on a ammonia-biodiesel duel-fuel engine modified from a heavy-duty diesel engine, delves into the impact of adjustments in the two-stage injection strategy on the combustion and emission characteristics.

To satisfy the power and energy requirement of the systems, such as electrical vehicles, the battery packs are constructed with hundreds of single cells connected in series and parallel connection. The most significant difference between a single cell and a battery pack is cell-to-cell variation. Not only does cell-to-cell variation have a big effect on the available energy and power of the battery packs, but also it causes early degradation of battery and potential safety issues. The cell variation effects on battery packs are widely studied because it is of great significance for battery sorting and management scheme. In this paper, battery pack inconsistency is clearly defined and the resulting battery capacity loss and aging acceleration problems are analyzed in detail. A comprehensive LiFePO4 battery pack model was established, which has taken into account cell-to-cell variation, thermal model, capacity degradation, resistance increasing and different battery topologies.

Designing an efficient cooling system with low power consumption is of high interest in the automotive engineering community. Heat generated due to the propulsion system and the on-board electronics in ground vehicles must be dissipated to avoid exceeding component temperature limits. In addition, proper thermal management will offer improved system durability and efficiency while providing a flexible, modular, and reduced weight structure. Traditional cooling systems are effective but they typically require high energy consumption which provides motivation for a paradigm shift. This study will examine the integration of passive heat rejection pathways in ground vehicle cooling systems using a “thermal bus”. Potential solutions include heat pipes and composite fibers with high thermal properties and light weight properties to move heat from the source to ambient surroundings.

This paper proposed a path coordination strategy for autonomous parking based on independently designed parking lot topological map. The strategy merges two types of paths at the three stages of path planning, to determinate mode switching timing between low-speed automated driving and automated parking. Firstly, based on the principle that parking spaces should be parallel or vertical to a corresponding path, a topological parking lot map is designed by using the point cloud data collected by LiDAR sensor. This map is consist of road node coordinates, adjacent matrix and parking space information. Secondly, the direction and lateral distance of the parking space to the last node of global path are used to decide parking type and direction at parking planning stage. Finally, the parking space node is used to connect global path and parking path at path coordination stage.

This paper has designed a real time control algorithm to use ISG motor actively compensate the torque ripple produced by the engine, to reduce torsional vibration. This paper consists of 3 parts. In the first section, this paper has introduced the research object and its modification for experiments. Then the development of control strategy is presented. The engine dynamic model is built, and real-time control with a feedforward unit and a feedback unit is derived. Encoder and cylinder pressure is used for engine torque estimator. Then the ISG motor output the counter-waveform to make the overall output smooth. In order to verify the effectiveness of the control strategy, the final section has established a test bench, where two experiments are carried out. One of the experimental conditions is to set the engine at a constant operating point, while the other is to crank the engine from 0 rpm to idle speed with ISG motor.

Advanced engine cooling systems can enhance the combustion environment, increase fuel efficiency, and reduce tailpipe emissions with less parasitic engine load. The introduction of computer controlled electro-mechanical valves, radiator fans, and coolant pumps require mathematic models and real time algorithms to implement intelligent thermal control strategies for prescribed engine temperature tracking. Smart butterfly valves can replace the traditional wax-based thermostat to control the coolant flow based on both engine temperature and operating conditions. The electric water pump and radiator fan replace the mechanically driven components to reduce unnecessary engine loads at high speeds and provide better cooling at low speeds.

Automated parking is an efficient way to solve parking difficulties and path planning is of great concern for parking maneuvers [1]. Meanwhile, the starting region of path planning greatly affects the parking process and efficiency. The present research of the starting region are mostly determined based on a single algorithm, which limits the flexibility and efficiency of planning feasible paths. This paper, taking parallel parking and vertical parking for example, proposes a method to calculate the starting region and select the most suitable path planning algorithm for parking, which can improve the parking efficiency and reduce the complexity. The collision situations of each path planning algorithm are analyzed under collision-free conditions based on parallel and vertical parking. The starting region for each algorithm can then be calculated under collision-free conditions.