Search Results

Energy management of battery electric vehicle (BEV) is a very important and complex multi-system optimisation problem. The thermal energy management of a BEV plays a crucial role in consistent efficiency and performance of vehicle in all weather conditions. But in order to manage the thermal management, it requires a significant number of temperature sensors throughout the car including high voltage batteries, thus increasing the cost, complexity and weight of the car. Virtual sensors can replace physical sensors with a data-driven, physical relation-driven or machine learning-based prediction approach. This paper presents a framework for the development of a neural network virtual sensor using a thermal system hardware-in-the-loop test rig as the target system. The various neural network topologies, including RNN, LSTM, GRU, and CNN, are evaluated to determine the most effective approach.

In modern powertrains systems, sensors are critical elements for advanced control. The identification of sensing requirements for such highly nonlinear systems is technically challenging. To support the sensor selection process, this paper proposes a methodology to quantify the information gained from sensors used to control nonlinear dynamic systems using a dynamic probabilistic framework. This builds on previous work to design a Bayesian observer to deal with nonlinear systems. This was applied to a bimodal model of the SCR aftertreatment system. Despite correctly observing the bimodal distribution of the internal Ammonia-NOx Ratio (ANR) state, it could not distinguish which state is the true state. This causes issues for a control engineer who is less interested in how precise a measurement is and more interested in the location within control parameter space. Information regarding the dynamics of the systems is required to resolve the bimodality.

Sensor selection for the control of modern powertrains is a recognised technical challenge. The key question is which set of sensors is best suited for an effective control strategy? This paper addresses the question through probabilistic modelling and Bayesian analysis. By quantifying uncertainties in the model, the propagation of sensor information throughout the model can be observed. The specific example is an abstract model of the slip behaviour of Selective Catalytic Reduction (SCR) DeNOx aftertreatment systems. Due to the ambiguity of the sensor reading, linearization-based approaches including the Extended Kalman Filter, or the Unscented Kalman Filter are not successful in resolving this problem. The stochastic literature suggests approximating these nonlinear distributions using methods such as Markov Chain Monte Carlo (MCMC), which is able in principle to resolve bimodal or multimodal results.

Modern control strategies for internal combustion engines use increasingly complex networks of sensors and actuators to measure different physical parameters. Often indirect measurements and estimation of variables, based off sensor data, are used in the closed loop control of the engine and its subsystems. Thus, sensor fusion techniques and virtual instrumentation have become more significant to the control strategy. With the large volumes of data produced by the increasing number of sensors, the analysis of sensor networks has become more important. Understanding the value of the information they contain and how well it is extracted through uncertainty quantification will also become essential to the development of control architecture. This paper proposes a methodology to quantify how valuable a sensor is relative to the architecture. By modelling the sensor network as a Bayesian network, Bayesian analysis and control metrics were used to assess the value of the sensor.

This study investigates the aerodynamic behavior of the flow around a rotating and stationary 60% scale isolated wheel, with and without the use of a moving ground plane. The aim of this research was to improve the understanding of the fundamental aerodynamic flow features around a wheel and to examine how rotation and moving ground planes modify these and affect the production of drag. A bespoke rotating wheel rig was designed and wind tunnel tests were performed over a range of pre to post critical Reynolds numbers. Force coefficients were obtained using balance measurements and flow field data were obtained using Particle Image Velocimetry (PIV). The unsteady flow field data generated was used to validate unsteady CFD predictions. These were performed using STAR-CCM+ and a k-ω SST Improved Delayed Detached Eddy Simulation (IDDES) turbulence model. This was seen to outperform other models by capturing an increased amount of finer detailed, high frequency vortical structures.

This literature review considers the problem of finding a suitable configuration of sensors and actuators for the control of an internal combustion engine. It takes a look at the methods, algorithms, processes, metrics, applications, research groups and patents relevant for this topic. Several formal metric have been proposed, but practical use remains limited. Maximal information criteria are theoretically optimal for selecting sensors, but hard to apply to a system as complex and nonlinear as an engine. Thus, we reviewed methods applied to neighboring fields including nonlinear systems and non-minimal phase systems. Furthermore, the closed loop nature of control means that information is not the only consideration, and speed, stability and robustness have to be considered. The optimal use of sensor information also requires the use of models, observers, state estimators or virtual sensors, and practical acceptance of these remains limited.

Sports Utility Vehicles (SUVs) often have blunt rear end geometries for design and practicality, which is not typically aerodynamic. Drag can be reduced with a number of passive and active methods, which are generally prioritised at zero yaw, which is not entirely representative of the “on road” environment. As such, to combine a visually square geometry (at rest) with optimal drag reductions at non-zero yaw, an adaptive system that applies vertical side edge tapers independently is tested statically. A parametric study has been undertaken in Loughborough University’s Large Wind Tunnel with the ¼ scale Windsor Model. The aerodynamic effect of implementing asymmetric side tapering has been assessed for a range of yaw angles (0°, ±2.5°, ±5° and ±10°) on the force and moment coefficients.

A novel semi-analytical solution has been developed for the calculation of the static and dynamic response of an off road tire interacting with a deformable terrain, which utilizes soil parameters independent of the size of the contact patch (size-independent). The models involved in the solution presented, can be categorized in rigid and/or pneumatic tires, with or without tread pattern. After a concise literature review of related methods, a detailed presentation of the semi-analytical solution is presented, along with assumptions and limitations. A flowchart is provided, showing the main steps of the numerical implementation, and various test cases have been examined, characterized in terms of vertical load, tire dimensions, soil properties, deformability of the tire, and tread pattern. It has been found that the proposed model can qualitatively capture the response of a rolling wheel on deformable terrain.

A control strategy has been designed for a city bus equipped with a pneumatic hybrid propulsion system. The control system design is based on the precise management of energy flows during both energy storage and regeneration. Energy recovered from the braking process is stored in the form of compressed air that is redeployed for engine start and to supplement the engine air supply during vehicle acceleration. Operation modes are changed dynamically and the energy distribution is controlled to realize three principal functions: Stop-Start, Boost and Regenerative Braking. A forward facing simulation model facilitates an analysis of the vehicle dynamic performance, engine transient response, fuel economy and energy usage.

The paper presents a measurement methodology which combines a fine-wire thermocouple with input reconstruction in order to measure crank angle resolved temperature in an engine air-intake system. Thermocouples that are of practical use in engine experiments tend to have a large time constant which affects measurement accuracy during rapid temperature transients. Input reconstruction methods have previously been applied to thermocouples but have not been specifically used in combination with an ultra-thin uninsulated wire thermocouple to investigate cyclic intake temperature behavior. Accurate measurement results are of interest to improve the validity of many crank-angle resolved engine models. An unshielded thermocouple sensor has been developed which is rigid enough to withstand the aerodynamic forces of the intake air.

Recovering the braking energy and reusing it can significantly improve the fuel economy of a vehicle which is subject to frequent braking events such as a city bus. As one way to achieve this goal, pneumatic hybrid technology converts kinetic energy to pneumatic energy by compressing air into tanks during braking, and then reuses the compressed air to power an air starter to realize a regenerative Stop-Start function. Unlike the pure electric or hybrid electric passenger car, the pneumatic hybrid city bus uses the rear axle to achieve regenerative braking function. In this paper we discuss research into the blending of pneumatic regenerative braking and mechanical frictional braking at the rear axle. The aim of the braking function is to recover as much energy as possible and at the same time distribute the total braking effort between the front and rear axles to achieve stable braking performance.

Previous research has highlighted that the formation of a sustained friction film, desired for stable and predictable friction performance, is highly dependent upon the region of the substrate (CMC) being examined. In attempt to improve the friction performance, notably bedding-in, research at LU has been developing coatings aimed at ensuring friction film development across the substrate. This paper focuses on the performance of one of these coating formulations, and examines the performance of this on a laboratory scale dynamometer. Subsequently, the coating has then been applied to a full size brake disc, as used on a prestige vehicle, for dynamometer testing at an industry scale for comparative purposes. On both lab and full scale samples the bedding performance shows improvements over the standard material, and at the full scale the coating indicates improved stability of subsequent friction performance through a modified AK Master test schedule.

This paper explores design methods for a vehicle handling dynamics state estimator based on a linear vehicle model. The state estimator is needed because there are some states of the vehicle that cannot be measured directly, such as sideslip velocity, and also some which are relatively expensive to measure, such as roll and yaw rates. Information about the vehicle states is essential for vehicle handling stability control and is also valuable in chassis design evaluation. The aim of this study is to compare the performance of a Kalman filter with that of a robust filter, under conditions which would be realistic and viable for a production vehicle. Both filters are thus designed and tested with reference to a higher order source model which incorporates nonlinear saturating tyre force characteristics. Also, both filters rely solely on accelerometer sensors, which are simulated with expected noise characteristics in terms of amplitude and spectra.