Advanced Propulsion Centre

The pursuit of high-energy lithium-ion batteries (LIBs) has driven the adoption of Ni-rich layered oxides such as NMC811, but their reactivity with conventional ethylene carbonate (EC)-based electrolytes leads to interfacial instability and performance loss. Here, we systematically investigate how electrolyte composition and additives influence the formation and stability of the solid electrolyte interphase (SEI) in graphite||single-crystal NMC811 cells. Using commercial-grade pouch cells under a consistent formation protocol, we combine operando acoustic emission (AE) and electrochemical atomic force microscopy (EC-AFM) with complementary electrochemical and structural characterisation to probe SEI formation in real time. We first assess common additives in EC-containing electrolytes, then progressively reduce EC content, and finally introduce SEI-forming additives into EC-free electrolytes. While EC promotes SEI growth on graphite, it also accelerates parasitic reactions at the Ni-rich cathode, resulting in limited gains in cycling stability. By contrast, EC-free electrolytes with targeted additives, particularly 10% fluoroethylene carbonate (FEC), form more uniform SEI layers, suppress gas evolution, and significantly enhance cycling stability, achieving 95.5% capacity retention after 300 cycles. This work demonstrates the dual role of EC in Ni-rich systems and highlights how correlative operando methods can guide rational electrolyte design for long-lived, high-energy LIBs.

This paper proposes a fully integrated bidirectional binary-tree multilevel converter that operates directly at the battery cell voltage level. By employing a novel binary-tree architecture as a voltage tap selector for multiple series-connected DC sources that provide discrete cell-level voltage steps, the proposed design enables the use of low-voltage, high-performance switches with reduced switching losses, controlled using a nearest-level modulation scheme. The topology can generate eight distinct voltage levels using only three control signals, and the number of switches in the conduction path scales logarithmically with the number of output voltage levels, minimizing series device count, conduction losses, and system complexity. The complete system, including power stages, control circuits, gate drivers, voltage level shifters, and protection circuits, is implemented in a 130-nm semiconductor process using junction-isolated lateral devices. The single-phase converter architecture allows dynamic selection of individual cell voltages to generate high-resolution quasi-sinusoidal or arbitrary waveforms, paving the way for building high-voltage, high-power, and distributed multilevel converters with superior power density and efficiency. Experimental results from a silicon prototype with a total chip area of 15 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{mm}^{2}$</tex-math></inline-formula> demonstrate the capability of the proposed converter to deliver multilevel output waveforms over a wide frequency range and input cell voltages, while supplying an output current of 2 A.

Switched reluctance motors (SRMs) have gained attention as a compelling alternative in electric motor technology due to their simple structure, robustness, and capability to operate at high speeds. Despite these advantages, SRMs face significant challenges, including high torque ripple, discrete torque generation, and the associated noise and vibrations. These drawbacks have inspired extensive research efforts to address and overcome these limitations. This study introduces an innovative approach to enhancing the rotor geometry of a three-phase double stator SRM (DSSRM). Through advanced geometric optimisation methods applied to a benchmark DSSRM, the proposed design delivers a substantial leap in performance. The optimised rotor achieves an impressive average torque of 347 Nm, compared to 243 Nm in the initial model, while effectively reducing the torque ripple from 45% to 21%. These results signify a transformative improvement in torque efficiency and ripple minimisation, highlighting the potential for further advancements in SRM technology and energy efficiency.

A novel artificial intelligence-based approach for the direct yaw control (DYC) of an all-wheel drive (AWD) electric vehicle (EV) is proposed in this paper. To improve adaptability and ability to handle nonlinearities via continuous learning, the proposed algorithm is built upon a twin delayed deep deterministic policy gradient (TD3) reinforcement learning (RL) algorithm for the optimal torque distribution across four wheels of the vehicle. The proposed model-free torque vectoring algorithm performs based on the interaction of an agent with an environment to learn the optimal policy in a reward-driven manner and obtain the ability to dynamically adapt to varying conditions, such as different roads and vehicle speeds. Unlike conventional control methods that rely on precise system modeling and may struggle to adapt under varying conditions, no model of the vehicle is required in the proposed method. This work proposes a model-free RL-based controller with curriculum learning to train the strategy, where the model learns simpler tasks first, progressively increasing difficulty to enhance stability and convergence. A detailed reward function and well-structured actor-critic networks are devised, and the proposed algorithm is compared with a conventional model-based linear quadratic regulator (LQR) approach. A nonlinear model with 7 degrees of freedom is used to model the dynamic behavior of the vehicle in MATLAB/Simulink, and the results are further verified through the implementation of IPG CarMaker under realistic driving scenarios. The performance of the proposed algorithm is studied across different maneuvers, demonstrating reduced yaw rate error and sideslip angle, resulting in enhanced dynamic stability.

This paper describes the magnetometer instrument (MAG) to be flown on the Cassini spacecraft. The instrument consists of two magnetometers and an on-board data processing unit. One magnetometer is a Vector Helium device of a type previously flown on Ulysses and several other missions which has been modified to operate in a Scalar mode providing measurements of the magnitude of the local magnetic field with very small absolute error. This is the first flight of such an instrument. The other magnetometer is a Fluxgate device of similar design to that flown on Ulysses and on many previous missions but with newly developed electronics. Both magnetometers can provide vector measurements of the three components of the magnetic field with a sensitivity of about 10pT. The unique combination of Fluxgate/Scalar or Fluxgate/Vector Helium operation offers increased possibilities for scientific investigation of the magnetic field environment in the Saturnian system. The data processing unit contains dual redundant systems based on the 80C86 microprocessor. It features sophisticated on-board data processing, large internal data storage capability and internal failure detection and recovery, giving the instrument the capability to operate autonomously for extended periods. The MAG instrument development team is drawn from institutes in four countries reflecting the multi-national flavor of the Cassini/Huygens mission.© (1996) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.