https://lutpub.lut.fi:443/handle/10024/158303 2026-06-07T14:17:21Z https://lutpub.lut.fi:443/handle/10024/172256 Design of a High-Specific-Power Traction Motor: Innovations and Strategies for Superior Performance Pyrhönen, Juha; Petrov, Ilya; Zadorozhniuk, Daniil; Laurila, Lasse; Parviainen, Miika; Goswami, Giota; Nutakor, Charles; Sopanen, Jussi; Martikainen, Iikka; Zeppei, Dieter; Bickel, Jonas; Pippuri-Mäkeläinen, Jenni; Keränen, Janne; Kinnunen, Kalle; Montonen, Juho In electric vehicle (EV) traction, energy conversion by electric motors must be material and energy efficient to reduce environmental burden caused by the manufacture and operation of the systems and to help in efficient transition towards net zero future. The scarcity of key materials like rare earths and to some extent copper must be addressed in the design. Novel traction motors need a different approach compared to traditional industrial motor designs. Here, we focus on innovative design and optimization strategies to enhance material and energy efficiency and meet the escalating demand for sustainable transportation solutions. We are developing a prototype motor that achieves a continuous specific power of 7 kW/kg, significantly exceeding current automotive standards. This will be achieved by elevated operational speed of the traction motor, integrating advanced materials, and innovative cooling techniques such as direct liquid cooling (DLC) using hollow hairpin conductors, and insulating them with polyether-ether-ketone (PEEK) extrusion and expandable mainwall insulation material. The approach reduces reliance on rare earth permanent magnet (PM) materials by 60% compared to existing motors, aligning with global sustainability objectives. Through simulation, modeling, and practical case studies, the research demonstrates the feasibility of these innovations in real-world applications, highlighting potential advancements in EV propulsion systems. The article not only represents the insights into the latest developments in the design of electric motors for EV but also delineates the journey towards the creation of such a motor with considering accompanying electromagnetic and mechanical challenges. 2025-07-16T00:00:00Z https://lutpub.lut.fi:443/handle/10024/172255 Two-Position Linear Electromagnetic Gear-Shifting Actuator and the Influence of Ferromagnetic Material Proximity on Its Performance Zadorozhniuk, Daniil; Petrov, Ilya; Kaasinen, Juho; Mattsson, Aleksi; Egorov, Dmitry; Lankila, Touko; Pyrhönen, Juha; Peltoniemi, Pasi This paper investigates a magnet-free, two-position electromagnetic gear-shifting actuator designed for mobile machinery applications. Unlike conventional hydraulic, pneumatic, or electromechanical systems, the proposed actuator employs tangential electromagnetic forces to engage and disengage gears without permanent magnets, thereby eliminating concerns about cost, sustainability and supply chains of the rare-earth materials. A systematic design methodology is presented, starting with simplified two-coil configurations and progressing to multi-coil topologies with advanced flux-guiding strategies - resembling a linear actuator. Axisymmetric finite-element simulations are used to evaluate force characteristics, transient dynamics, and sensitivity to environmental variations. Particular emphasis is placed on the influence of surrounding ferromagnetic structures, which are shown to significantly alter the magnetic flux distribution and reduce actuation force if not properly accounted for. Case studies demonstrate that while two- and three-coil designs suffer from force sags and premature flux leakage, a multi-coil (e.g. six-coil) topology can sustain the required engagement forces in most environments, though performance degrades under full ferromagnetic enclosure. Results indicate that actuator performance strongly depends on spatial clearance from ferromagnetic components, rotor mass, and coil switching dynamics. The findings provide practical design guidelines for integrating magnet-free electromagnetic shifters into electric vehicle drivetrains, highlighting both the opportunities for sustainable materials and the challenges of flux management in constrained environments. 2026-01-12T00:00:00Z https://lutpub.lut.fi:443/handle/10024/172254 Advantages of E-trailers Suojansalo, Rasmus; Aarniovuori, Lassi; Lindh, Pia; Peltoniemi, Pasi Heavy-duty trucks consist of a truck unit and a semi-trailer. In addition to the truck unit, the semi-trailers can be made electric with an electrical energy storage (EES) and an electric axle (e-axle). The resulting system is called an e-trailer, which can be combined with any kind of truck unit. When an e-trailer is combined with an internal combustion engine (ICE) truck unit, the resulting system functions as a hybrid vehicle. This configuration offers reduced fuel consumption, which also lowers greenhouse gas emissions and fuel costs. Alternatively, combining an e-trailer with an electric truck unit increases the truck's capacity and driving range. This allows longer haulages and can reduce infrastructure needed for charging the truck’s batteries. The system has some drawbacks, such as the initial cost of the e-trailer and the increased mass. This study evaluates the benefits of an e-trailer compared to a regular trailer with both an ICE and electric truck unit 2025-02-11T00:00:00Z https://lutpub.lut.fi:443/handle/10024/172253 Physically informed input design for neural friction models in mechanical simulations Nguyen, Tien Vuong; Han, Seongji; Wojtyra, Marek; Mikkola, Aki; Orzechowski, Grzegorz Friction models are essential to accurate mechanical simulation, but classical analytical laws are often difficult to calibrate and can induce numerical stiffness. Data-driven surrogate models offer an attractive alternative, yet require appropriate input selection. This work demonstrates that kinematic-only inputs are insufficient to uniquely predict friction force, especially in the static and stick-slip regimes, and proposes an improved input-output design for neural-network-based friction surrogates. A feed-forward neural network with temporal input windows was trained to perform one-step-ahead prediction. Training data were generated using four classical friction models: smoothed Coulomb, Brown–McPhee, Dahl, and LuGre. To assess generalization, the trained models were evaluated on a Rabinowicz-type spring-mass-on-a-treadmill system and a low-velocity reversal benchmark without additional training or fine-tuning. Using the combined histories of relative velocity and force, the network reproduced all four friction models with high accuracy, achieving R-squared scores between 0.993 and 0.9999 and capturing both sliding and stick-slip behaviors. Conversely, velocity-only networks failed to replicate bristle-type models, confirming the non-uniqueness of purely kinematic mapping. These results provide a practical recipe for designing friction surrogates that generalize across different mechanical configurations. 2026-05-29T00:00:00Z