Improved decision support for engine-in-the-loop experimental design optimization

Experimental optimization with hardware in the loop is a common procedure in engineering and has been the subject of intense development, particularly when it is applied to relatively complex combinatorial systems that are not completely understood, or where accurate modelling is not possible owing to the dimensions of the search space. A common source of difficulty arises because of the level of noise associated with experimental measurements, a combination of limited instrument precision, and extraneous factors. When a series of experiments is conducted to search for a combination of input parameters that results in a minimum or maximum response, under the imposition of noise, the underlying shape of the function being optimized can become very difficult to discern or even lost. A common methodology to support experimental search for optimal or suboptimal values is to use one of the many gradient descent methods. However, even sophisticated and proven methodologies, such as simulated annealing, can be significantly challenged in the presence of noise, since approximating the gradient at any point becomes highly unreliable. Often, experiments are accepted as a result of random noise which should be rejected, and vice versa. This is also true for other sampling techniques, including tabu and evolutionary algorithms. After the general introduction, this paper is divided into two main sections (sections 2 and 3), which are followed by the conclusion. Section 2 introduces a decision support methodology based upon response surfaces, which supplements experimental management based on a variable neighbourhood search and is shown to be highly effective in directing experiments in the presence of a significant signal-to-noise ratio and complex combinatorial functions. The methodology is developed on a three-dimensional surface with multiple local minima, a large basin of attraction, and a high signal-to-noise ratio. In section 2, the methodology is applied to an automotive combinatorial search in the laboratory, on a real-time engine-in-the-loop application. In this application, it is desired to find the maximum power output of an experimental single-cylinder spark ignition engine operating under a quasi-constant-volume operating regime. Under this regime, the piston is slowed at top dead centre to achieve combustion in close to constant volume conditions. As part of the further development of the engine to incorporate a linear generator to investigate free-piston operation, it is necessary to perform a series of experiments with combinatorial parameters. The objective is to identify the maximum power point in the least number of experiments in order to minimize costs. This test programme provides peak power data in order to achieve optimal electrical machine design. The decision support methodology is combined with standard optimization and search methods – namely gradient descent and simulated annealing – in order to study the reductions possible in experimental iterations. It is shown that the decision support methodology significantly reduces the number of experiments necessary to find the maximum power solution and thus offers a potentially significant cost saving to hardware-in-the-loop experi- mentation

A systematic review of lumped-parameter equivalent circuit models for real-time estimation of lithium-ion battery states

This paper presents a systematic review for the most commonly used lumped-parameter equivalent circuit model structures in lithium-ion battery energy storage applications. These models include the Combined model, Rint model, two hysteresis models, Randles' model, a modified Randles' model and two resistor-capacitor (RC) network models with and without hysteresis included. Two variations of the lithium-ion cell chemistry, namely the lithium-ion iron phosphate (LiFePO4) and lithium nickel-manganese-cobalt oxide (LiNMC) are used for testing purposes. The model parameters and states are recursively estimated using a nonlinear system identification technique based on the dual Extended Kalman Filter (dual-EKF) algorithm. The dynamic performance of the model structures are verified using the results obtained from a self-designed pulsed-current test and an electric vehicle (EV) drive cycle based on the New European Drive Cycle (NEDC) profile over a range of operating temperatures. Analysis on the ten model structures are conducted with respect to state-of-charge (SOC) and state-of-power (SOP) estimation with erroneous initial conditions. Comparatively, both RC model structures provide the best dynamic performance, with an outstanding SOC estimation accuracy. For those cell chemistries with large inherent hysteresis levels (e.g. LiFePO4), the RC model with only one time constant is combined with a dynamic hysteresis model to further enhance the performance of the SOC estimator

Measurement and characterisation technique for real-time die temperature prediction of MOSFET-based power electronics

This paper presents a technique to predict the die temperature of a MOSFET based on an empirical model derived following an offline thermal characterization. First, a method for the near-simultaneous measurement of die temperature during controlled power dissipation is presented. The method uses a linear arbitrary waveform power controller which is momentarily disconnected at regular intervals to allow the forward voltage drop of the MOSFET's antiparallel diode to be measured. Careful timing ensures the power dissipation is not significantly affected by the repeated disconnection of the power controller. Second, a pseudorandom binary sequence-based system identification approach is used to determine the thermal transfer impedance, or cross coupling between the dice of two devices on shared cooling using the near-simultaneous measurement and control method. A set of infinite impulse response digital filters are fitted to the cross-coupling characteristics and used to form a temperature predictor. Experimental verification shows excellent agreement between measured and predicted temperature responses to power dissipation. Results confirm the usefulness of the technique for predicting die temperatures in real time without the need for on-die sensors

A Multiobjective G.A./Fuzzy Logic augmented flight controller for an F16 aircraft.

An investigation is made in this paper of the pos- sibility of enhancing the performance of controllers of unstable systems while retaining safety critical function. In this case, a General Dynamics F16 fighter is considered in simulation. A fuzzy logic controller is designed and its membership functions tuned by Multiobjective Genetic Algorithms in order to design an augmented flight controller with enhanced manouverability which still retains safety critical operation. The controller is assessed in terms of pilot effort and thus reduction of pilot fatigue. The controller is incorporated into a six degree of freedom real-time flight simulator, and flight tested by a qualified pilot instructor

Power management analysis of a photovoltaic and battery energy storage-based smart electrical car park providing ancillary grid services

Future car parks will require significant power to support electric vehicle (EV) charging as there will be both an increase in the penetration of EVs and a higher demand for charging power as battery packs increase in capacity. The effective management of the charging and local battery storage can be installed to help prevent excessive increases in electrical feeder capacity; however, it is inevitable that car parks will attain significant power capability in the future. There is therefore an opportunity for car park owners to utilise this and generate additional revenue by providing frequency response services to the electrical grid. This paper describes the modelling of a car park that utilises photovoltaic power generation, battery storage, and EV charging management strategies to provide a grid frequency response service. The analysis using simulated car park data shows that it can provide a high availability in terms of service but it is dependent on the capacity of the installed generation and storage

Battery energy storage systems for the electricity grid: UK research facilities

Grid-connected battery energy storage systems with fast acting control are a key technology for improving power network stability and increasing the penetration of renewable generation. This paper describes two battery energy storage research facilities connected to the UK electricity grid. Their performance is detailed, along with hardware results, and a number of grid support services are demonstrated, again with results presented. The facility operated by The University of Manchester is rated at 236kVA, 180kWh, and connected to the 400V campus power network, The University of Sheffield operates a 2MVA, 1MWh facility connected to an 11kV distribution network

Collinear mecanum drive: modelling, analysis, partial feedback linearisation, and nonlinear control

The Collinear Mecanum Drive (CMD) is a novel robot locomotion system, capable of generating omnidirectional motion whilst simultaneously dynamically balancing, achieved using a collinear arrangement of three or more Mecanum wheels. The CMD has a significantly thinner ground footprint than existing omnidirectional locomotion methods, which does not need to be enlarged with increasing robot height as to avoid toppling during acceleration or external disturbance. This combination of omnidirectional manoeuvrability and a thin ground footprint allows for the creation of tall robots that are able to navigate through much narrower gaps between obstacles than existing omnidirectional locomotion methods. This allows for greater manoeuvrability in confined and cluttered environments, such as that encountered in the personal service and automated warehousing robotics sectors. This article derives the kinematics and dynamics models of the CMD, analyses controllability and accessibility, and determines the degree to which a CMD can be linearised by feedback. A partial feedback linearisation is then performed, and three practically useful nonlinear controllers are derived using a backstepping design approach, all with convergence and stability guarantees for the fully-coupled nonlinear model. These are demonstrated both in simulation and on a real-world CMD experimental prototype

Electrical modelling of a DC railway system with multiple trains

Electrical modelling of rail tracks with multiple running trains is complex due to the difficulties in solving the power flow. The train positions, speed and acceleration are constantly varying resulting in a nonlinear system. In this work, a method is proposed for modelling DC electric railways to support power flow analysis of a simulated metro train service. The method exploits the MathWorks simulation tool Simscape, using it to model the mechanical and electrical characteristics of the rail track system. The model can be simulated to provide voltages at any position in the track and additionally, the voltages seen by any train. The model includes regenerative braking on trains, this is demonstrated to cause overvoltage in the feeding line if it is higher than the power demand of the other trains at that time. Braking resistors are used to protect the network from overvoltage by burning the excess energy. Through the implementation of Energy Storage Systems (ESSs), it will be possible to improve the energy efficiency and remove timetabling restrictions of electric railways by effectively controlling the rail track voltage. The paper proposes several methods to validate the model

A controlled migration genetic algorithm operator for hardware-in-the-loop experimentation

In this paper, we describe the development of an extended migration operator, which combats the negative effects of noise on the effective search capabilities of genetic algorithms. The research is motivated by the need to minimize the num- ber of evaluations during hardware-in-the-loop experimentation, which can carry a significant cost penalty in terms of time or financial expense. The authors build on previous research, where convergence for search methods such as Simulated Annealing and Variable Neighbourhood search was accelerated by the implementation of an adaptive decision support operator. This methodology was found to be effective in searching noisy data surfaces. Providing that noise is not too significant, Genetic Al- gorithms can prove even more effective guiding experimentation. It will be shown that with the introduction of a Controlled Migration operator into the GA heuristic, data, which repre- sents a significant signal-to-noise ratio, can be searched with significant beneficial effects on the efficiency of hardware-in-the- loop experimentation, without a priori parameter tuning. The method is tested on an engine-in-the-loop experimental example, and shown to bring significant performance benefits

Nox1/Ref-1-mediated activation of CREB promotes Gremlin1-driven endothelial cell proliferation and migration.

Pulmonary arterial hypertension (PAH) is a complex degenerative disorder marked by aberrant vascular remodeling associated with hyperproliferation and migration of endothelial cells (ECs). Previous reports implicated bone morphogenetic protein antagonist Gremlin 1 in this process; however, little is known of the molecular mechanisms involved. The current study was designed to test whether redox signaling initiated by NADPH oxidase 1 (Nox1) could promote transcription factor CREB activation by redox factor 1 (Ref-1), transactivation of Gremlin1 transcription, EC migration, and proliferation. Human pulmonary arterial EC (HPAECs) exposed in vitro to hypoxia to recapitulate PAH signaling displayed induced Nox1 expression, reactive oxygen species (ROS) production, PKA activity, CREB phosphorylation, and CREB:CRE motif binding. These responses were abrogated by selective Nox1 inhibitor NoxA1ds and/or siRNA Nox1. Nox1-activated CREB migrated to the nucleus and bound to Ref-1 leading to CREB:CRE binding and Gremlin1 transcription. CHiP assay and CREB gene-silencing illustrated that CREB is pivotal for hypoxia-induced Gremlin1, which, in turn, stimulates EC proliferation and migration. In vivo, participation of Nox1, CREB, and Gremlin1, as well as CREB:CRE binding was corroborated in a rat PAH model. Activation of a previously unidentified Nox1-PKA-CREB/Ref-1 signaling pathway in pulmonary endothelial cells leads to Gremlin1 transactivation, proliferation and migration. These findings reveal a new signaling pathway by which Nox1 via induction of CREB and Gremlin1 signaling contributes to vascular remodeling and provide preclinical indication of its significance in PAH