A large number of battery pack returns from electric vehicles (EV) is expected for the next years, which requires economically efficient disassembly capacities. This cannot be met through purely manual processing and, therefore, needs to be automated. The variance of different battery pack designs in terms of (non-) solvable fitting technology and superstructures complicate this. In order to realize an automated disassembly, a computer vision pipeline is proposed. The approach of instance segmentation and point cloud registration is applied and validated within a demonstrator grasping busbars from the battery pack. To improve the sorting of the battery pack components to achieve high-quality recycling after the disassembly, a labeling system containing the relevant data (e.g., cathode chemistry) about the battery pack is proposed. In addition, the use of sensor-based sorting technologies for peripheral components of the battery pack is evaluated. For this purpose, components such as battery pack and module housings of multiple manufacturers were investigated for their variation in material composition. At the current stage, these components are usually produced as composites, so that, for a high-quality recycling, a pre-treatment may be necessary.

The temperature of a lithium-ion battery is a crucial parameter for understanding the internal processes during various operating and failure scenarios, including thermal runaway. However, the internal temperature is comparatively higher than the surface temperature. This particularly affects cells with a large cross-section, which is due to heat development within the cell and lower heat dissipation due to a poorer ratio of volume to surface area. This paper presents an approach that enables real-time monitoring of the behavior of a commercial prismatic high-energy battery cell (NMC811/C, 95 Ah, Contemporary Amperex Technology Co., Limited (Ningde, China)) in the event of thermal runaway induced by overcharging. The internal cell temperature is investigated by the subsequent integration of two hard sensors between the two jelly rolls and additional sensors on the surface of the aluminum housing of the battery cell. The sensor’s signals show a significant increase in the temperature gradient between the temperature in the core of the cell and the cell casing surface until the onset of venting and thermal runaway of the battery. The data enable a detailed investigation of the behavior of the battery cell and the comparatively earlier detection of the point of no return in the event of thermal runaway.

A methodology for the development of nonlinear model predictive control for thermal management of a 40-ton fuel-cell heavy-duty truck is presented, using the medium-temperature cooling circuit as a case study. The approach integrates control-oriented modeling, parameter estimation, and experimental validation based on drivetrain test bench measurements under controlled high-temperature ambient conditions. A lumped-parameter model of the medium-temperature circuit, including coolant, oil, electric motors, and power-electronics auxiliaries, is derived and implemented in a Simulink environment, with heat-transfer parameters calibrated from test bench data and radiator air-side resistance and fan characteristics derived from CFD simulations and manufacturer specifications, respectively. Model parameters are identified using a systematic estimation procedure and the resulting model is validated against long-duration roller test measurements, achieving coefficients of determination above R² = 0.9 and normalized RMSE values below 10% for all key temperatures. The validated model is then used as the prediction model in a model predictive controller that manipulates radiator fan and coolant-pump speeds, while treating component heat losses, vehicle speed and ambient temperature as measured disturbances. The controller is evaluated in a model-in-the-loop environment for representative long-haul and urban driving cycles and different ambient temperatures, and its performance is benchmarked against conventional rule-based and PI-based control strategies. Depending on the driving cycle and ambient conditions, the proposed NMPC reduces cooling system energy consumption by up to 39.6% compared to a PI controller (VECTO Urban Delivery cycle, 35 °C ambient), with an average reduction of 16.6% across all investigated driving cycles and ambient conditions, without a significant increase in average or maximum coolant temperature. The proposed methodology provides a transferable workflow for developing predictive thermal management control in fuel-cell heavy-duty vehicles and other complex automotive cooling systems.

Electric vehicles offer a sustainable alternative to combustion engines but still face range and safety limitations. Integrating battery cells as structural elements (Cell-to-X, CTX) is a promising route to address both, but requires cells that reliably withstand everyday mechanical loads. This study presents a non-destructive mechanical characterization of commercial 4680-format cylindrical lithium-ion cells (Supplier 1), combining quasi-static compression tests (hemispherical and cylindrical indenters) across state of charge (SOC) and indentation depth, cyclic aging over up to 150 cycles, and post-mortem computed tomography (CT) analysis. Deformation remained predominantly reversible up to indentation depths of 0.5 mm for the hemispherical and 1.8 mm for the cylindrical indenter, and peak forces of up to 9.16 kN were sustained without functional or electrical failure, although localized plastic casing deformation was observed in individual cells at the highest tested depths. For the cylindrical indenter, peak force increased consistently with SOC and indentation depth, consistent with prior literature, while temperature and voltage remained stable throughout loading, with no immediate evidence of an internal short circuit. For the cells with a sufficiently complete cycling record, capacity loss over the valid cycles amounted to 1.8–2.9% per 100 cycles for the mechanically loaded cells, compared with 2.0% for an unstressed reference cell. Post-mortem CT imaging confirmed deformation remained confined to the outer casing and outermost jelly-roll layers without affecting internal structure or performance. These results provide preliminary evidence that the tested cells can tolerate structurally relevant mechanical loads without loss of function, supporting further development of CTX architectures.

Lithium-ion batteries (LIBs) are considered a pivotal enabling technology for the advancement of transportation electrification, the development of stationary energy storage systems, and the integration of renewable energy sources. Despite continuous advances in energy density and cell design, the fast-charging capability of LIBs is still significantly constrained by temperature-dependent electrochemical and thermal processes, which are crucial to the overall performance of the battery. The objective is to quantify the temperature-dependent fast-charging potential and charging power capability. This is achieved by experimentally analyzing the internal resistance (IR) during the charging of three large-format prismatic state-of-the-art cells, in addition to extrapolating their charging power capability and constant-current constant-voltage charging behavior under varying thermal boundary conditions. Utilizing the IRs ascertained during the charging process, the fast-charging potential (FCP) is introduced as a dimensionless metric that expresses the temperature-induced increase in the maximum theoretically achievable charging current at a given SoC, relative to 25 °C. Elevated cell temperatures are found to reduce IR and thus increase the theoretically achievable charging power, with the FCP nearly independent of SoC but cell-specific, ranging from about 18% (CATL) to 40% (SVolt) for a temperature increase from 25 °C to 45 °C. This potential can be exploited in two ways: firstly, by increasing the C-rate while maintaining the same constant-current duration; and secondly, by extending the constant-current phase at a constant initial C-rate. The proposed methodology facilitates a systematic and comparative evaluation of the maximum theoretically attainable temperature-dependent fast-charging potential of LIBs. It provides a foundation for the development of thermally optimized fast-charging strategies.