The heavy-duty freight sector finds itself in a state of change. Legislation and customer demand pushes the industry towards electrification, where the pure battery and fuel cell electric hybrid have emerged as the top contending technologies to replace the conventional diesel powertrain, and although pure battery powertrains dominate the light-duty sector, projections indicate that fuel cell hybrids will show superior performance in long-range missions with heavy cargo. Particularly so in the context of future autonomous vehicles, considering the potential for continuous, non-stop driving. Regardless, several techno-economic challenges remain before widespread adoption of either pure battery powertrains or fuel cell hybrids in the heavy-duty sector. These challenges include but are not limited to the weight of lithium cells, elevated hydrogen prices, insufficient recharging/refuelling infrastructure, durability, as well as thermal management related issues.

A model-based approach can be used to target these challenges, which motivated the development of the Electrochemical Commercial Vehicle (ECCV) platform; a model library tailored for controls algorithm development and rapid virtual prototyping of electrified trucks. While auto-manufacturers use in-house and proprietary software to solve similar tasks, the open-source nature of the ECCV-platform allows for collaboration within academia and industry without issues relating to intellectual property rights, a type of collaboration which was demonstrated in a benchmark competition held at the IFAC World Congress 2023, where six teams from universities all over the world contributed their solutions to the fuel cell hybrid energy management problem.

Although the development of the ECCV-platform constitutes a major part of the thesis, further work was done to improve its capacity. First, the platform was extended with the capability to model state-of-the-art thermal systems, specifically through the inclusion of refrigerant models. This extension allowed for an investigation into the effectiveness of various heat pump systems for pure battery trucks. Secondly, the platform was extended with a fuel cell model validated in the intermediate temperature range, which enabled an investigation of how elevated stack temperatures may benefit overall system efficiency. Thirdly, the model library was used to develop a real-time energy management algorithm for fuel cell trucks, demonstrating the value of the platform also in the context of optimal control.

Clustering is an important tool in data-driven fault diagnosis to make use of unlabeled data. Collecting representative data for fault diagnosis is a difficult task since faults are rare events. In addition, using data collected from the field, e.g., logged operational data and data from different workshops about replaced components, can result in labelling uncertainties. A common approach for fault diagnosis of dynamic systems is to use residual-based features that filter out system dynamics while being sensitive to faults. The use of conventional clustering algorithms is complicated by that the distribution of residual data from one fault class varies for different realizations and system operating conditions. In this work, a clustering algorithm is proposed for residual data that clusters data by estimating fault signatures in residual space. The proposed clustering algorithm can be used on time-series data by clustering batches of data from the same fault scenario instead of clustering data sample-by-sample. The usefulness of the proposed clustering algorithm is illustrated using residual data from different fault scenarios collected from an internal combustion engine test bench. Copyright (C) 2022 The Authors.

Turbocharger testing is a time consuming process, and as rapid-prototyping technology advances, so must other areas in the development chain. As an example, in one study a compressor map took over 34 hours to measure. In this paper, an effort to combat the main bottleneck of turbocharger testing, namely the thermal inertia, is made. When changing operating point during the measurement process, several minutes can be required before the turbocharger components reach temperature steady state. In an earlier paper, a method based on non-linear trajectory optimization was developed that significantly reduced the testing time required to produce compressor performance maps. The time was reduced by a factor of over 60, compared to waiting for the system to reach steady state with constant inputs. However, the method required a model of the turbocharger. This paper extends the method with system identification and model predictive control (MPC). This is an important step in order to use the optimal control method when only geometric information of the turbocharger is known, such as new prototypes. To demonstrate the effectiveness of the combination of system identification, non-linear trajectory optimization and MPC, the control strategy is applied to a virtual gas-stand implemented as a Simulink model, based on data from a Mitsubishi TD04 turbocharger. The data that was used to create the model was originally collected at Saab Automobile in Trollhättan, 2011. The results show that system identification captures the turbocharger behavior. Trajectory optimization finds a set of time optimal input trajectories. MPC successfully tracks the generated references. Real time implementation of the Matlab/Simulink based algorithm is planned for experimental testing.