Browse Topic: Coolants
Linear time-invariant (LTI) reduced-order models (ROMs) have been widely used in battery thermal management simulations due to their low hardware requirements, high computational efficiency, and good accuracy. However, the inherent assumption of LTI behavior limits their applicability in scenarios with varying coolant flow rates, where this assumption is no longer valid. To address this limitation, a novel ROM is developed by decomposing the entire battery thermal system into two subsystems. All solid components are modeled as a traditional LTI ROM, while the coolant channel is represented using Newton’s cooling law. The two subsystems are then coupled through the exchange of heat transfer rate and temperature at the fluid–solid interface between the coolant and the cold plate. Model fidelity is further enhanced by introducing a spatially distributed heat flux during the generation of the LTI ROM for solid components. Validation is performed against CFD simulations at both module and pack levels, under constant and varying flow rates. The results demonstrate that the proposed ROM achieves high accuracy while requiring several orders of magnitude less computational time than the corresponding CFD models.
This paper presents Nexifi11D, a simulation-driven, real-time Digital Twin framework that models and demonstrates eleven critical dimensions of a futuristic manufacturing ecosystem. Developed using Unity for 3D simulation, Python for orchestration and AI inference, Prometheus for real-time metric capture, and Grafana for dynamic visualization, the system functions both as a live testbed and a scalable industrial prototype. To handle the complexity of real-world manufacturing data, the current model uses simulation to emulate dynamic shopfloor scenarios; however, it is architected for direct integration with physical assets via industry-standard edge protocols such as MQTT, OPC UA, and RESTful APIs. This enables seamless bi-directional data flow between the factory floor and the digital environment. Nexifi11D implements 3D spatial modeling of multi-type motor flow across machines and conveyors; 4D machine state transitions (idle, processing, waiting, downtime); 5D operational cost breakdowns covering electricity, tooling, labour, coolant, and depreciation; 6D AI/ML-based failure prediction using temperature and pressure inputs; 7D predictive downtime triggers based on learned thresholds; 8D sustainability analytics measuring CO₂ emissions per motor; 9D workforce optimization via virtual shift scheduling and fatigue simulation; 10D supply chain resilience through simulated part delays and buffer modeling; and 11D risk and quality management using defect simulation and risk scoring. All data are generated live and visualized through Grafana dashboards, enabling real-time monitoring of OEE, energy use, defects, and AI-based alerts. Nexifi11D establishes a unified, cyber-physical platform for intelligent, sustainable, and predictive manufacturing, making multidimensional factory optimization practically demonstrable within one connected environment.
In the evolving landscape of energy efficiency and sustainability, understanding machine behavior in real-world operating conditions is essential. This solution introduces a data-driven Energy Management Dashboard designed to analyze and report critical machine parameters by leveraging LFI (Leverage Fleet Intelligence) and LFI Data (Local Field Intelligence Data). The tool serves as a robust solution for engineering and operations teams to gain actionable insights into machine performance and exposure. By tracking key parameters—such as engine fan speed, coolant temperature, and machine speed—across a fleet of machines (with support for over 1100 unique signals), the solution enables real-time monitoring and historical analysis. It helps identify when parameters go outside their specified limits and assesses the resulting impact on overall machine performance. The core functionality includes: Monitoring machine operating conditions under real field environments. Correlating parameter anomalies with performance degradation. Identifying exposure and usage trends based on location and operating conditions. Recommendations on potential impact of parameter value variations on the Engine Torque, malfunction in specific features of the tractor (machine). This solution architecture integrates seamlessly with existing data pipelines and leverages LFI data for contextual insights. The development process involved collaboration with the Ruse squad to ensure relevance to on-field challenges. The expected outcomes include improved visibility into machine usage, early detection of potential issues, and enhanced data-driven decision-making for field operations and energy management. By transforming raw machine data into clear visual insights, this solution empowers teams to take proactive measures in improving efficiency and reliability.
A cold start occurs when the engine is cranked after being off for a long time, enough for its temperature to drop down to the cold ambient levels. Cold start in an engine is a critical phase as it is characterized by elevated emissions. During a cold start, exhaust components such as catalytic converter do not operate in its optimal temperature zone leading to reduced efficiency in emission control. New regulations for engine emissions are becoming stringent for this condition, hence it is important to accurately determine cold start condition in an engine to optimize the emissions control strategy. Accurate engine off time calculation plays a crucial role in cold start detection, emissions control and On-Board Diagnostics (OBD-II) decision making. This engine off time if greater than 6 hours indicates one of the conditions to confirm a cold start. Other conditions such as Ambient temperature and coolant temperature along with the engine off time confirms a cold start. This paper presents a novel approach to calculate engine off time without any need for supplementary new hardware, leveraging detection of cold start to meet the new requirements for Cold start emission reduction strategy (CSERS) for OBD-II diagnostics. The proposed methodology utilizes Real time clock to estimate the time difference between a successful Engine Cranking and previous engine off to accurately estimate engine off time, enabling precise differentiation between a cold and a warm start.
BATSS project objective is to design a safe, effective and sustainable battery pack. To achieve this, the battery system (BS) will be mechanically, electrically and thermally optimized using cutting edge technology. Consequently, the battery system includes innovative 4695 cylindrical cells and advanced thermal management, carried out with the Miba FLEXCOOLER®. This work focuses on the BS thermal optimization using system simulation tools. First a simplified version of the BS is simulated with all physical phenomena involved in thermal behavior to identify first order parameters. It appears that various BS component and heat transfer can be neglected in comparison with the heat transfer due to cooling system. Then the simulation of the full battery system is conducted under nominal condition. Cooling system appears to be performant as it allows a controlled averaged temperature and very low cell-to-cell temperature variability. Finally, impact of both design and operating parameters is evaluated. Simulation shows that the coolant mass flow can be reduced by 70% from its nominal value allowing to maintain good thermal performances while reducing the pressure drop in the cooling system. Impact of the Miba FLEXCOOLER® / cell surface exchange is also investigated. Results demonstrate that increasing exchange surface reduces averaged temperature in the pack but slightly increases cell temperature heterogeneities.
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