Browse Topic: Thermal management
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of LTI behavior limits their applicability in scenarios with varying coolant flow rates, where this assumption is no longer valid.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
This paper presents the emissions development of a heavy-duty hydrogen internal-combustion engine (H₂ICE) targeting ultra-low NOx with a design goal of 20 mg/hp-hr. The approach integrates advanced thermal management of the engine and aftertreatment, including engine out NOx management through air-fuel ratio controls and an electric heater to accelerate catalyst light-off and sustain activity at low-load/idle conditions. A diesel-derived aftertreatment system (ATS) is selected to maximize practicality and component commonality, and an integrated controls strategy spanning the engine and ATS is implemented to demonstrate ultra-low NOx capability over EPA certification cycles. The paper concludes with considerations for periodic SCR regeneration to ensure emission compliance.
This study presents a fully integrated, vehicle-level thermal management model for gasoline fuel tanks, designed to predict transient fuel temperatures, tank wall heating, and vapor generation under real-world driving conditions. The model simulates coupled thermal contributions from exhaust radiation, transient underbody airflow, conductive heat transfer, in-tank pump heating, and dynamic changes in fuel composition and level. Validation against on-road measurements shows strong agreement for fuel temperature and vapor flow profiles. Results confirm that exhaust radiative heating is the dominant thermal load, particularly during the post-shutdown heat soak period. A well-designed heat shield reduced peak tank wall temperature by approximately 27 °C, significantly lowering fuel heating and evaporation. Parametric analysis indicates that while fuel Reid Vapor Pressure (RVP) and tank material influence evaporation, their effect is secondary to external heat mitigation. While this model
A computational study based on a conjugate heat transfer (CHT) method in SimericsMP+ was performed to predict the winding temperatures in an X76 emotor. In this study, the thermal load was represented in the simulation through the solution of electromagnetic equations in SimericsMP+, where heat generation was driven by root-mean-square (RMS) current, while liquid cooling was applied at flow rates ranging from 1 LPM to 6 LPM. Simulations were conducted to measure the temperature on three thermocouple locations on each side of the winding crown and weld regions under steady operation. The computational strategy employed a loosely coupled approach. A fluid-only simulation was first carried out to establish stable flow conditions, followed by coupling with solid conduction where the winding acted as the heat source. The predicted temperature distributions were then compared with test data. Results obtained show good agreement, with differences remaining within an acceptable range, thereby
The reliability of Drive Unit (DU) oil pumps is critical to the performance and safety of electric vehicles, as these pumps provide essential lubrication and thermal management. In modern EV architectures, real-time health monitoring of these pumps typically relies on indirect signals than dedicated sensing hardware, a design choice optimized for cost, weight, and system complexity. This makes early fault detection a non-trivial challenge. To address this limitation, we present a novel, data-driven anomaly detection framework that leverages large-scale customer fleet telemetry and advanced machine learning to identify incipient pump degradation that traditional diagnostic methods often fail to capture. Specifically, we develop an XGBoost regression model trained on time-series features—including commanded pump speed, oil temperature, and historical pump current—to predict expected current behavior under nominal conditions. Deviations are quantified using the Mean Absolute Percentage
Electric Vehicles (EVs) are rapidly transforming the automotive landscape, offering a cleaner and more sustainable alternative to internal combustion engine vehicles. As EV adoption grows, optimizing energy consumption becomes critical to enhancing vehicle efficiency and extending driving range. One of the most significant auxiliary loads in EVs is the climate control system, commonly referred to as HVAC (Heating, Ventilation, and Air Conditioning). HVAC systems can consume a substantial portion of the battery's energy—especially under extreme weather conditions—leading to a noticeable reduction in vehicle range. This energy demand poses a challenge for EV manufacturers and users alike, as range anxiety remains a key barrier to widespread EV acceptance. Consequently, developing intelligent climate control strategies is essential to minimize HVAC power consumption without compromising passenger comfort. These strategies may include predictive thermal management, cabin pre-conditioning
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