Improvements in the efficiency of combustion within a vehicle can lead to reductions in the emission of harmful pollutants and increased fuel efficiency. Gas sensors have a role to play in this process, since they can provide real time feedback to vehicular fuel and emissions management systems as well as reducing the discrepancy between emissions observed in factory tests and ‘real world’ scenarios. In this review we survey the current state-of-the-art in using porous materials for sensing the gases relevant to automotive emissions. Two broad classes of porous material – zeolites and metal–organic frameworks (MOFs) – are introduced, and their potential for gas sensing is discussed. The adsorptive, spectroscopic and electronic techniques for sensing gases using porous materials are summarised. Examples of the use of zeolites and MOFs in the sensing of water vapour, oxygen, NO_x, carbon monoxide and carbon dioxide, hydrocarbons and volatile organic compounds, ammonia, hydrogen sulfide, sulfur dioxide and hydrogen are then detailed. Both types of porous material (zeolites and MOFs) reveal great promise for the fabrication of sensors for exhaust gases and vapours due to high selectivity and sensitivity. The size and shape selectivity of the zeolite and MOF materials are controlled by variation of pore dimensions, chemical composition (hydrophilicity/hydrophobicity), crystal size and orientation, thus enabling detection and differentiation between different gases and vapours.

This paper presents two torque estimation methods for vehicle engines: unknown input observer (UIO) and adaptive parameter estimation. We first propose a novel yet simple unknown input observer based on the crankshaft rotation dynamics only. For this purpose, an invariant manifold is derived by defining auxiliary variables in terms of first-order low-pass filters, where only one constant (filter coefficient) needs to be tuned. These filtered variables are used to calculate the estimated torque. Robustness of this UIO against sensor noise is studied and compared to two other estimators. On the other hand, since the engine torque dynamics can be formulated as a parameterized form with unknown time-varying parameters, we further present several adaptive laws for time-varying parameter estimation. The parameter estimation errors are derived to drive these adaptive laws and time-varying adaptive gains are introduced. The two proposed estimators only use the measured air mass flow rate and engine speed, and thus allow for improved computational efficiency. Both estimators are verified via a dynamic engine simulator built in a commercial software GT-Power, and also practically tested via experimental data collected in a dynamometer test-rig. Both simulations and practical tests show very encouraging results with small estimation errors even in the presence of sensor noise.

There is increasing popularity of using permanent magnet (PM) machines in vehicle propulsion systems. Due to the pre-excitation of PM, the machines could offer higher efficiency and torque density. However, their inherent torque ripple could be problematic for electric vehicles (EVs) due to low damping of torsional vibration which may affect passenger comfort. This can prohibit the use of PM machines to improve vehicle energy efficiency. This paper presents the application of resonant control (RC) to suppress the impact of the PM torque ripple, which aims to reduce the vibration of a vehicle. A prototype PM machine and driveline have been fitted to a light-duty off-road vehicle. Firstly, the drivetrain frequency response was analyzed. The main source of the vibration is identified as the 24th harmonic torque ripple of the 12-slot/8-pole PM machine, which originates from the cogging torque and air-gap flux harmonics. The vibration becomes more severe when the torque ripple frequency is close to the natural frequency of the drivetrain. Then, the RC in conjunction with PI controller (PIR) was introduced to be used at the outer speed loop, so that an accurate current reference signal can be generated for the inverter to mitigate the speed ripple. When compared to the conventional Proportional-Integral (PI) controller, PIR controller has demonstrated over 80% reduction in speed ripple, even when the torque ripple frequency is close to the natural frequency of the vehicle.

Axial flux permanent magnet (AFPM) machines are promising for hybrid electric vehicles (HEVs) due to the compactness, high torque density and high efficiency. However, poor thermal characterization leads to an over-sizing of these machines which ultimately compromises overall system efficiency. In this paper, the transient thermal behavior of all the components in the single sided AFPM machine are characterized in an accurate but computationally efficient lumped parameter thermal model (LPTM). For the first time, contact measurements on the rotor have been used in AFPM machines to demonstrate the ability of the model to predict all component temperatures to within 4 °C for steady state. The mean temperature error over a load step transient was less than 5°C with a maximum error less than 13.5 °C which was for the winding. The model has a running time of approximately 1000 times faster than real time on a desktop machine and is suitable for integration into system simulation tools and predictive control strategies to avoid over-sizing of the motor and improve the usage of the electric machine in dynamic duty cycles.

This brief addresses the emission reduction of spark ignition engines by proposing a new control to regulate the air-fuel ratio (AFR) around the ideal value. After revisiting the engine dynamics, the AFR regulation is represented as a tracking control of the injected fuel amount. This allows to take the fuel film dynamics into consideration and simplify the control design. The lumped unknown engine dynamics in the new formulation are online estimated by suggesting a new effective unknown system dynamics estimator. The estimated variable can be superimposed on a commercially configured, well-calibrated gain scheduling like proportional-integral-differential (PID) control to achieve a better AFR response. The salient feature of this proposed control scheme lies in its simplicity and the small number of required measurements, that is, only the air mass flow rate, the pressure and temperature in the intake manifold, and the measured AFR value are used. Practical experiments on a Tata Motors Limited two-cylinder gasoline engine are carried out under a realistic driving cycle. The comparative results show that the proposed control can achieve an improved AFR control response and reduced emissions.