Cell-to-cell manufacturing variability analysis in liquid-cooled module battery pack for automotive transportation

Physics-informed cross-attention operator network with hard-constrained Fourier features for heat map prediction of large-scale battery packs

Synergistic effects of Fibonacci-inspired turbulators and hybrid nanofluids on thermal regulation in Li-ion battery packs

Enhancing temperature uniformity of lithium-ion battery packs: A numerical study on an immersion cooling system with a coolant distribution manifold

Hybrid propulsion systems increase ship energy efficiency by allowing the sharing of power between diesel engines and battery energy storage systems. However, the long-term efficiency of these types of systems depends on accurately estimating the Remaining Useful Life (RUL) of lithium-ion batteries to allow effective charge scheduling, maintenance planning, and reliable navigation. This study uses nine data-driven algorithms, including ensemble methods, recurrent neural networks, and linear models, to examine the RUL of a lithium-ion battery pack installed on a hybrid cargo ship. A 5-fold cross-validation structure was used to preprocess, normalize, and analyze actual operational data gathered during the vessel's service life. To improve the accuracy of predictions, hyperparameter optimization was performed out. Long Short-Term Memory (LSTM), which reduced MAE from 2.87 to 1.46 and RMSE from 12.57 to 6.34 after optimization while retaining a high coefficient of determination (R² = 0.9999), performed the best among the models that were evaluated. The results obtained indicate that condition-based maintenance and energy utilization methods on hybrid ships can be effectively supported by data-driven RUL estimation. In order to enhance generalization and assess integration with real-time propulsion control systems, future research will expand the analysis to multi-vessel datasets.

This study investigates a hybrid tab-cooled heat sink combining sintered-wick copper heat pipes embedded in a high-conductivity aluminum block with computer numerical control machined serpentine liquid cooling channels for a 15-cell lithium-ion battery pack (capacity: 2.5 Ah per cell, parallel connection). Performance was evaluated under constant-current discharge rates (1, 3, and 4 C, where C denotes nominal capacity rating) and automotive drive cycles (Urban Dynamometer Driving Schedule (UDDS), Short Dynamometer Driving Schedule (SDDS), and Worldwide Harmonized Light Vehicles Test Procedure) at coolant flow rates from 0 to 0.5 liters per minute. At 3 C discharge, peak temperature decreased from 81.6 °C to 62.9 °C with active cooling, extending safe operation from 50 to 93% depth-of-discharge before reaching the 60 °C threshold. Temperature variation among cells was reduced by 40%. Dynamic drive cycles showed peak temperature reductions of 19 °C (UDDS) and 22 °C (SDDS). Thermal resistance analysis revealed maximum reductions of 66% (1 C) and 39% (3 C) versus baseline. At low discharge rates, the system is convection-limited, making liquid cooling highly effective. However, at high rates (3–4 C), heat pipe operational limits become the primary constraint, indicating applications requiring sustained operation above 3 C would benefit more from enhanced heat pipe capacity than from increased liquid cooling flow rates.

The increasing integration of renewable energy necessitates battery energy storage systems (BESS) to ensure grid stability. To achieve higher voltages, multiple battery packs are typically connected in series, making state-of-charge (SOC) balancing among different packs critical for safe and stable operation. This paper employed the Switch-Multiplexing coupled Multi-Resonant Switched-Capacitor Converter (SMX-MRSCC) topology for battery equalization due to its cost-effectiveness achieved through switching devices reuse. However, this multiplexing also introduces coupling in SOC control between packs. Conventional strategies rely on continuous regulation of phase-shift ratios for decoupled control, often compromising soft-switching operation and increasing power reflux. To address this issue, a novel phase-shift control strategy is proposed in this paper. The phase-shift ratios are discretized into three predefined states: forward/reverse states and an idle state, optimized to achieve low reflux power, and low RMS current and peak current under soft-switching constraints. Specifically, to realize efficient equalization by fixed operating states, the state selection for phase-shift ratios of every two adjacent half-bridge is dynamically determined based on the accumulated imbalance energy (AIE) of the upstream battery packs, which minimizes energy interaction through path optimization. Therefore, the proposed approach enhances equalization efficiency by component-level optimal design and system-level energy transfer path optimization. Experimental results demonstrate the effectiveness of the proposed method in achieving efficient SOC balancing for series-connected battery packs.

• A novel disassembly process reasoning method combines KG-driven GAT with information decomposition modular. • The SURD mechanism enables the network structure for semantic information and relation analysis. • KG-driven process reasoning provides the decision support for disassembly task allocations and tool selections. • A practical demonstration is accomplished through the overall disassembly strategy of the battery pack. Due to the complex structures and heterogeneous information inherent in End-of-Life (EOL) products, determining optimal disassembly solutions based on Human-Robot Collaboration (HRC) remains a challenging task. As structural and functional uncertainties in EOL products increase, traditional disassembly approaches struggle to meet the practical disassembly demands. Although various algorithms have been proposed for optimizing disassembly processes, significant challenges persist. These include the limited adaptability of existing models and difficulties in representing dynamic structured information effectively. To address these challenges, this study proposes a novel method combining knowledge graph-driven neural networks with an information decomposition module. This mechanism enables the network to discover structural semantic information and relational connections, facilitating the prediction of optimal disassembly strategies and enhancing the process reasoning capability of EOL product data and knowledge. Similarly, the proposed method provides reliable decision support for HRC disassembly task allocations and tool selections, enabling efficient and safe disassembly operations within complex disassembly processes. Finally, we demonstrate the method’s efficacy by using an example of an EOL battery pack, reasoning optimal disassembly strategies and potential process relations in the complex HRC disassembly scenario.

Thermo-fluid and structural optimization of hybrid air-assisted CPCM battery thermal management for electric vehicles

Immersion cooling battery thermal management system design and optimization for high-energy-density battery packs: A comparative study with side cooling plates

This paper investigates optimal control strategies for charging and discharging battery packs, aiming to maximize lifespan and performance. The focus is on developing efficient techniques based on Genetic Algorithms (GAs) and machine learning (ML) to optimize battery pack operation. This study uses a proposed GA as a global search engine for optimal control parameters, while integrating Support Vector Machine (SVM) to improve the prediction accuracy of the battery state affected by these parameters. Furthermore, the Deep Reinforcement Learning (DRL) agent is trained in a physics-based simulation environment, such as PyBaMM, directly learning physics-informed, dynamic charging current profiles, unlike traditional DRL studies.The research explores various control parameters, including charging/discharging rates, current profiles, and temperature management, to minimize degradation and maximize energy efficiency. This approach effectively searches the vast solution space to identify optimal control strategies that balance immediate energy demands with long-term battery health. Simulation results demonstrate the effectiveness of the proposed GA-based optimization framework in achieving significant improvements in battery pack lifespan, energy efficiency, and overall performance compared to conventional control methods.

Thermal performance of immersion cooling system with swallowtail inspired flow guide for lithium-ion battery packs

Side pole crash performance of battery electric vehicles

Individual cell surveillance in lithium-ion battery systems has not yet been widely adopted in the industry due to the increased cost of the final product. Thereby, the limited number of temperature sensors in a battery pack endangers system safety. This paper proposes talkative power conversion for collecting the temperature data from the sensors of a large-format battery cell, utilizing the high-frequency signal induced by the power converter modulation. Such a battery is called talkative battery in this paper. The principle of load shift keying (LSK) is utilized to establish a communication link between the sensor and the power converter (e.g., a battery charger) via the wire line. The concept of talkative battery is theoretically analyzed and experimentally validated with two different lithium iron phosphate (LFP) battery cells. The findings indicate that low-cost individual cell thermal sensing is viable with minimal hardware requirements.

Modeling and mitigation of jet-flame-driven fire propagation in double-layer lithium-ion battery packs for heavy-duty electric vehicles

Design matters: The influence of EV battery pack design for disassembly on environmental and circularity impact

A metaheuristic SBO-based thermal control strategy for heavy duty electric vehicle batteries

Thermal monitoring of Li-ion battery pack using uniform and gradient hemispherical protrusions and rectangular patterns cooling system

Simulation study on the cooling performance of immersion cooling systems for energy storage battery pack

Thermal management of composite phase change material capsule-based lithium-ion battery packs under forced convection