Industrial applications such as automation, robots, and electric vehicles are widely employing permanent magnet synchronous motors (PMSM) due to inherent characteristics such as high torque to power ratio, low maintenance, high efficiency, and wide operating range. However, PMSM drives can suffer from unwanted torque ripples caused by periodic disturbances of the electromagnetic effect. First, this article analyzes the sources of torque ripple in PMSMs. Second, the torque measurement techniques of the PMSM including torque sensors, torque observers, and indirect measurement methods are discussed to highlight their shortcomings. Third, this article categorizes and summarizes advanced control-based torque ripple minimization (CB-TRM) techniques for the PMSM by critically evaluating their impacts. These CB-TRM techniques actively control the current waveforms to ensure smooth torque generation and mitigate the effects of torque ripples. Finally, the article concludes by discussing remaining research questions and opportunities for future research on TRM techniques for PMSMs.

The development, validation and control of a bi-mode train model is presented. A detailed modular model of a United Kingdom Class 800 train, which included carbon dioxide emissions data, was developed in MATLAB/Simulink. This model was validated against data obtained from a full day of rail journeys in the south-west of England. The validated model was used to develop control measures to reduce the carbon dioxide emissions of the train. Combining adaptive speed limit control with selective engine shutdown reduced the carbon dioxide emissions by 19.1% over a representative route without affecting the train’s on-time performance. The model was used to develop a tool for investigating the emissions benefits of (intermittent/discontinuous) route electrification. This tool shows that electrification of a route can reduce the carbon dioxide emissions by 66%.

This paper presents an end-to-end approach, named FrictionSegNet, for jointly estimating tyre-road friction coefficient and identifying road surfaces in real time from on board camera data. FrictionSegNet combines semantic segmentation and friction estimation by learning a shared latent space that encompasses both semantic segmentation and friction coefficient information. An objective function is designed for this task and minimised using *geco to train the model, providing the ability to control the balance between improved predictions and uncertainty measurement. To the best of our knowledge, this study is the first attempt to jointly estimate tyre-road friction and surface type by learning the joint latent space of semantic segmentation and friction coefficient information. The results suggest that it is possible to identify low-friction surfaces, e.g. snow or ice, and estimate upcoming road friction in real time from a camera only. As it is of interest to develop techniques that require less training data, numerical experiments were performed using transfer learning from a dataset consisting of images of various road surfaces. This led to better performance and faster convergence during training. FrictionSegNet achieved per-pixel accuracies of $97\%$ and $95\%$ when identifying snow and ice respectively, and RMS errors of $0.04$ – $0.09$ when estimating $\mu$ values achievable by a truck *abs on gravel, dry and wet asphalt, snow, and ice surfaces.

A near-term strategy to reduce emissions from rail vehicles, as a path to full electrification for maximal decarbonisation, is to partially electrify a route, with the remainder of the route requiring an additional self-powered traction option. These rail vehicles are usually powered by a diesel engine when not operating on electrified track and are referred to as bi-mode vehicles. This paper analyses the benefits of discontinuous electrification compared to continuous electrification using the CO₂ estimates from a validated high-fidelity bi-mode (diesel-electric) rail vehicle model. This analysis shows that 50% discontinuous electrification provides a maximum of 54% reduction in operational CO₂ emissions when compared to the same length of continuously electrified track. The highest emissions savings occurred when leaving train stations where vehicles must accelerate quickly to line speed. These results were used to develop a linear regression model for fast estimation of CO₂ emissions from diesel running and electrification benefits. This model was able to estimate the CO₂ emissions from a route to within 10% of that given by the high-fidelity model. Finally, additional considerations such as cost and the embodied CO₂ in electrification infrastructure were analysed to provide a comparison between continuous and discontinuous electrification. Discontinuous electrification can cost up to 56% less per reduction in lifetime emissions than continuous electrification and can save up to 2.3 times more lifetime CO₂ per distance electrified.