Thomas Steffen

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.

Electric machines are highly efficient and highly controllable actuators, but they do still suffer from a number of imperfections. One of them is torque ripple, which introduces high frequency harmonics into the motion. One (cost- and performance-neutral) countermeasure is to apply control that counters the torque ripple. This paper compares several single-input single-output (SISO) control approaches for feedback control of torque ripple of a Permanent Magnet Synchronous Machine (PMSM). The baseline is PI (proportional-integral) control, which does not suppress torque ripple, and the most popular control approach is proportional-integral resonant (PIR) control. Both are compared to an advanced PIR controller (PIRA), frequency modulation, a mixed sensitivity design, and an iterative learning controller (ILC). The analysis demonstrates that PIR control, mixed sensitivity state feedback, and the modulating controller achieve identical behaviour. The choice between these three options is therefore dependent on preferences for the design methodology, or on implementation factors. The PIRA and the ILC on the other hand show more sophisticated behaviour that may be advantageous for certain applications, at the expense of higher complexity.

This article develops an optimal predictive power management strategy (OP-PMS) for plug-in hybrid electric vehicles (PHEVs) to manage the remaining battery level throughout a defined journey. Apart from using the battery before charging it again at the destination, this approach also supports transit through an Ultra Low Emission Zone (ULEZ), where a PHEV needs to operate in pure electric mode to avoid emissions. This type of PHEV power management strategy (PMS) design problem is a noncausal control problem, where the power demand of the remaining driving cycle and the final battery energy level targets influence the current power management action. Rather than solving the whole optimization problem online, this article presents a simplification that leads to a closed-form analytic solution with parameters that can be computed offline. This article makes three key contributions. First, a novel input-linearization method is developed, which converts the OP-PMS into a convex quadratic programming (QP) problem that captures the essential model nonlinearities using an input transformation and a quadratic cost function. Second, the influence of future power demands and final targets on current energy management policy is explicitly quantified from a dynamic optimal control perspective. Third, the noncausal convex QP is approximated with a closed-form analytic solution that is calculated offline. This leads to a real-time implementable OP-PMS with coefficients that can be precalculated and stored in the engine control unit. To demonstrate the viability of this approach, numerical examples are provided based on a generic PHEV model to verify the efficacy of the proposed OP-PMS.

As the market share of electric vehicles increases, the intermittent load on the electricity grid due to charging will increase. This can be counteracted by Vehicle-to-Grid (V2G) which utilises dormant electric vehicles to feed power into the grid, generating income for the vehicle owner while relieving load across the grid. However, increased battery use through V2G can negatively affect battery health. In this work, a computational model of an electric vehicle with battery degradation is used to investigate the relationship of these effects. The analysis was conducted at the top level of detail, only considering the battery pack of the vehicle. The findings of this investigation show that the cost relating to battery degradation is smaller than the potential profit available from Vehicle-to-Grid over a three-year period. However, the benefit does not seem to be enough to justify the upfront investment requirement, and further financial incentives, such as net billing, may be required to make V2G economically viable. Future development within this field is vital for the success of the electric vehicle within the automotive markets, and for the transition to a renewable energy grid.

Electrical turbocharger assist is one of the most critical technologies in improving fuel efficiency of conventional powertrain vehicles. However, strong challenges lie in high efficient operations of the device due to its complexity. In this paper, an integrated framework on characterization, control, and testing of the electrical turbocharger assist is proposed. Starting from a physical characterization of the engine, the controllability and the impact of the electrical turbocharger assist on fuel economy and exhaust emissions are both analyzed. A multivariable robust controller is designed to regulate the dynamics of the electrified turbocharged engine in a systematic approach. To minimize the fuel consumption in real time, a supervisory level controller is designed to update the setpoints of key controlled variables in an optimal way. Furthermore, a cutting-edge experimental platform based on a heavy-duty diesel engine is built. The proposed framework has been evaluated in simulations, physical simulations, and experiments. Results are presented for the developed system and the proposed framework that demonstrate excellent tracking performance, high robustness, and the potential for improvements in fuel efficiency.

The integration of selective catalytic reduction catalysts (SCR) into diesel particulate filters (DPF) as a way to treat nitrogen oxides (NOx) and particulate matter (PM) emission is an emerging technology in diesel exhaust aftertreatment. This is driven by ever-tightening limits on NOx and PM emission. In an integrated SCR-in-DPF (also known as SCRF®, SCR-on-DPF, SDPF, or SCR coated filter), the SCR catalyst is impregnated within the porous walls of the DPF. The compact, low weight/volume of the integrated unit provides improvement in the diesel engine cold start emission performance. Experimental investigations have shown comparable performance with standard SCR and DPF units for NOx conversion and PM control, respectively. The modelling of the integrated unit is complicated. The main challenge is how to best capture the complexity of the physical phenomena-e.g., the competition of soot oxidation and SCR reaction for available NO2, or the interaction of washcoat loading on deNOx performance, PM filtration efficiency and system pressure drop-in a simplified but adequate representation. This paper reviews the approaches to SCR-in-DPF modelling obtained from published materials. It discusses the specific considerations given to adapting coated (catalysed) DPF model for SCR-in-DPF modelling. This includes characterisation of SCR model and associated kinetics, inclusion of ammonia dynamics and interaction with soot in the transport model, and careful model calibration to capture the salient phenomena in the system. This work presents current research in SCR-in-DPF modelling and highlights potential areas of future research. It can serve as a resource for new researchers in SCR-in-DPF modelling

The goal of grid friendly charging is to avoid putting additional load on the electricity grid when it is heavily loaded already, and to reduce the cost of charging to the consumer. In a smart metering system, Day Ahead tariff (DA) prices are announced in advance for the next day. This information can be used for a simple optimization control, to select to charge at cheapest times. However, the balance of supply and demand is not fully known in advance and the Real-Time Prices (RTP) are therefore likely to be different at times. There is always a risk of a sudden price change, hence adding a stochastic element to the optimization in turn requiring dynamic control to achieve optimal time selection. A stochastic dynamic program (SDP) controller which takes this problem into account has been made and proven by simulation in a previous paper.Since there are differences between the DA and the RTP tariff, this paper proposes a (1) predictor to create an unbiased estimate of the RTP tariff based on available data. It uses a regression on historical data to find the best prediction of the expected price. Finally, a (2) case study based on data from the Illinois Electricity Grid prices is presented to validate the SDP controller over several years of data. The stochastic optimization uses the RTP prices effectively, getting very close to the globally optimal charging price. However, the predictor achieves only a slight reduction in prediction uncertainty with this data sate, and it has a negligible effect on cost. This means that DA prices can be used as a fair prediction of RTP charging cost here. The SDPM successfully reacts in the case study and leads to savings on charging costs over the years presented.