Modelling and Control of Narrow Tilting Vehicle for Future Transportation System

The increasing number of cars leads traffic congestion and parking problems in urban area. Small electric four-wheeled narrow tilting vehicles (NTV) have the potential to become the next generation of city cars. However, due to its narrow width, the NTV has to lean into corners like two-wheeled vehicles during a turn. It is a challenge to maintain its roll stability to protect it from falling down. This chapter aims to describe the development of NTV and drive assistance technologies in helping to improve the stability of an NTV in turning. The modelling of an NTV considers the dynamics of the tyres and power train of the vehicle. A nonlinear tilting controller for the direct tilting control mechanism is designed to reduce the nonlinear behaviour of an NTV operating at different vehicle velocities. In addition, two torque vectoring based torque controllers are designed to reduce the counter-steering process and improve the stability of the NTV when it turns into a corner. The results indicate that the designed controllers have the ability to reduce the yaw rate tracking error and maximum roll rate. Then riders can drive an NTV easily with the drive assistance system

Torque vectoring based drive assistance system for turning an electric narrow tilting vehicle

The increasing number of cars leads to traffic congestion and limits parking issue in urban area. The narrow tilting vehicles therefore can potentially become the next generation of city cars due to its narrow width. However, due to the difficulty in leaning a narrow tilting vehicle, a drive assistance strategy is required to maintain its roll stability during a turn. This article presents an effective approach using torque vectoring method to assist the rider in balancing the narrow tilting vehicles, thus reducing the counter-steering requirements. The proposed approach is designed as the combination of two torque controllers: steer angle–based torque vectoring controller and tilting compensator–based torque vectoring controller. The steer angle–based torque vectoring controller reduces the counter-steering process via adjusting the vectoring torque based on the steering angle from the rider. Meanwhile, the tilting compensator–based torque vectoring controller develops the steer angle–based torque vectoring with an additional tilting compensator to help balancing the leaning behaviour of narrow tilting vehicles. Numerical simulations with a number of case studies have been carried out to verify the performance of designed controllers. The results imply that the counter-steering process can be eliminated and the roll stability performance can be improved with the usage of the presented approach

Nonlinear PI control for variable pitch wind turbine

Wind turbine uses a pitch angle controller to reduce the power captured above the rated wind speed and release the mechanical stress of the drive train. This paper investigates a nonlinear PI (N-PI) based pitch angle controller, by designing an extended-order state and perturbation observer to estimate and compensate unknown time-varying nonlinearities and disturbances. The proposed N-PI does not require the accurate model and uses only one set of PI parameters to provide a global optimal performance under wind speed changes. Simulation verification is based on a simplified two-mass wind turbine model and a detailed aero-elastic wind turbine simulator (FAST), respectively. Simulation results show that the N-PI controller can provide better dynamic performances of power regulation, load stress reduction and actuator usage, comparing with the conventional PI and gain-scheduled PI controller, and better robustness against of model uncertainties than feedback linearization control

Sensitivity Analysis to Reduce Duplicated Features in ANN Training for District Heat Demand Prediction

Artificial neural network (ANN) has become an important method to model the nonlinear relationships between weather conditions, building characteristics and its heat demand. Due to the large amount of training data required for ANN training, data reduction and feature selection are important to simplify the training. However, in building heat demand prediction, many weather-related input variables contain duplicated features. This paper develops a sensitivity analysis approach to analyse the correlation between input variables and to detect the variables that have high importance but contain duplicated features. The proposed approach is validated in a case study that predicts the heat demand of a district heating network containing tens of buildings at a university campus. The results show that the proposed approach detected and removed several unnecessary input variables and helped the ANN model to reduce approximately 20% training time compared with the traditional methods while maintaining the prediction accuracy. It indicates that the approach can be applied for analysing large number of input variables to help improving the training efficiency of ANN in district heat demand prediction and other applications

An adaptive power distribution scheme for hybrid energy storage system to reduce the battery energy throughput in electric vehicles

The battery/supercapacitor (SC) hybrid energy storage system (HESS) is widely applied in electric vehicles (EVs) in recent years due to the hybrid system which combines the benefits of both devices. This paper proposes an adaptive power distribution scheme for battery/SC HESS to maximise the usage of SC according to its stored energy and load current. In the approach, the low-pass filter is developed with adaptive algorithm to calculate the suitable cut-off frequency to allocate the power demand between the battery and SC. The approach can adjust the cut-off frequency but not change the structure of the control system, and thus its original property of simple implementation and stability is not affected. The comprehensive simulation study verifies the effectiveness of the proposed adaptive power distribution scheme in a battery/SC HESS and its stability is further validated using Lyapunov method. The result shows that the adaptive method performs better than a traditional control system with 20%–40% less battery energy throughput during operation and can adjust the dynamic response of the HESS according to the energy capacity of SC to further improve system efficiency. The proposed adaptive power distribution scheme is verified able to extend the service life of the HESS system in EV applications

Nonlinearity compensation based tilting controller for electric narrow tilting vehicles

Considering the traffic congestion and low energy consumption, small electric four-wheeled narrow tilting vehicles (NTV) are expected to be the new generation of city cars. In order to maintain lateral stability, the NTVs should have to lean into corners like two-wheeled vehicles. This is a challenge to keep a NTV stable during turning at different speeds. This paper aims to design a nonlinearity compensation based tilting controller for the direct tilting mechanism based NTVs. The controller adaptively compensates the nonlinearities of NTV roll dynamics in different vehicle speeds without the accurate vehicle models and, consequently, improve its robustness to rider’s behaviour. By utilising the proposed nonlinear tilting control system, both new riders and experienced riders can drive the NTVs easily with improved tilting stability. Simulations have been conducted to validate the applicability and robustness of the proposed control approach

Current Distribution and Anode Potential Modelling in Battery Modules with a Real-World Busbar System

The performance of a lithium-ion battery pack is not only related to the behavior of the individual cells within the pack, but also presents a strong interdependency with the temperature distributions, interconnect resistance between cells, and the cell’s physical location within the complete battery pack. This paper develops representative busbar circuits with different fidelities to simulate the behavior of cells within a battery module and analyses the influence of cell-to-cell heat transfer and interconnect resistance on the distribution of cell current and anode potential in a battery module. This work investigates multi-physics interactions within the battery module, including cells, interconnect resistances, and temperature distributions, while analyzing the lithium plating problem at the module level. Specifically, the cell model used in this study is a validated thermally coupled single-particle model with electrolyte, and the battery module uses a commercially representative busbar design to include 30-cells in parallel. The effects of parameter changes within the battery pack on individual cells are simulated and analyzed. The study highlights that some cells in the battery module would present a higher risk of lithium plating during fast-charge conditions as they experience a lower anode potential during the charge events

Speed sensorless nonlinear adaptive control of induction motor using combined speed and perturbation observer

High performance induction motors (IM) require a robust and reliable speed controller to maintain the speed tracking performance under various uncertainties and disturbances. This paper presents a sensorless speed controller for IM based on speed and perturbation estimation and compensation. By defining a lumped perturbation term to include all unmodeled nonlinear dynamics and external disturbances, two state and perturbation observers are designed with combining the model reference adaptive system (MRAS) based speed observer to estimate the flux and speed states and the flux- and speed-loop related lumped perturbation terms. The estimated flux, speed and perturbation terms are used to design an output feedback, speed sensorless nonlinear adaptive controller (SSNAC) for IM. The stability of the closed-loop system is addressed in Lyapunov theory. Effectiveness of the SSNAC is verified via simulation and experiment tests. Comparing with the standard vector control plus MRAS speed observer (VC-MRAS), the proposed SSNAC reduces the speed tracking error by 20% to 30% on average under model uncertainties and unknown load disturbance due to the estimation and compensation of perturbation terms. The combined observer can estimate the real rotor speed under speed varying and load changes and thus makes SSNAC achieve high performance robust speed drive without using speed sensors

Design of robust MPPT controller for grid-connected PMSG-Based wind turbine via perturbation observation based nonlinear adaptive control

This paper presents a robust maximum power point tracking (MPPT) control scheme for a grid-connected permanent magnet synchronous generator based wind turbine (PMSG-WT) using perturbation observation based nonlinear adaptive control. In the proposed control scheme, system nonlinearities, parameter uncertainties, and external disturbances of the PMSG-WT are represented as a lumped perturbation term, which is estimated by a high-gain perturbation observer. The estimate of the lumped perturbation is employed to compensate the actual perturbation and further achieve adaptive feedback linearizing control of the original nonlinear system, without requiring the detailed system model and full state measurements. The effectiveness of the proposed control scheme is verified through both simulation studies and experimental tests. The results show that, compared with the conventional vector controller and the standard feedback linearizing controller, the proposed control strategy provides higher power conversion efficiency and has better dynamic performances and robustness against parameter uncertainties and external disturbances