Search Results (7)

Given the rising importance of cost-effective solutions in battery research, this study employs an accessible testing approach using low-cost, sensor-equipped platforms that enable broader research and educational applications. It presents a comparative evaluation of lithium-ion battery degradation under two charging strategies: static charging (constant current at 1.2 A) and dynamic charging (stepped current from 400 mA to 800 mA) over 200 charge–discharge cycles. A custom-built, low-cost test platform based on an ESP32 microcontroller was developed to provide real-time monitoring of voltage, current, temperature, and internal resistance, with automated control and cloud-based data logging. The results indicate that static charging provides greater voltage stability and a lower increase in internal resistance (9.3%) compared to dynamic charging (30.17%), suggesting reduced electrochemical stress. Discharge time decreased for both strategies, by 6.25% under static charging and 18.46% under dynamic charging, highlighting capacity fade and aging effects. Internal resistance emerged as a reliable indicator of degradation, closely correlating with reduced runtime. These findings underscore the importance of selecting charging profiles based on specific application needs, as dynamic charging, while offering potential thermal benefits, may accelerate battery aging. Furthermore, the low-cost testing platform proved effective for long-term evaluation and degradation analysis, offering an accessible alternative to commercial battery cyclers. The insights gained contribute to the development of adaptive battery management systems that optimize performance, lifespan, and safety in electric vehicle applications. Full article

Electric vehicles have recently gained greater attention across all countries for transportation purposes in on-road and off-road forms due to their supreme performance and clean eco-friendliness status. Lithium-ferrous phosphate batteries are the primary energy storage devices in electric vehicles due to their higher energy density, longer lifespan, and lower self-discharge rate. They also possess several technical advantages, including a wider range of applications, economic affordability, an environmentally friendly nature, and, most importantly, superior electrochemical performance, which makes them a strong competitor to lead acid batteries. In the present study, a performance and health assessment of a lithium ferrous phosphate battery (LFP) pack consisting of 23 cells connected in series balancing mode with a 7360 Wh maximum energy storage capacity has been carried out at various current ranges of operation such as 3 A, 5 A, and 8 A in a typically developed battery management system to estimate their optimized performance and overall health conditions. Further study has been conducted to investigate the characteristics of LFP packs under various power-mode conditions, ranging from 20 W to 750 W. This experimental study revealed that the LFP battery pack exhibits a remarkable state-of-charge capability, achieving 58% charging in a 3.3-h runtime period. A similar decreasing trend was also observed during power-mode operations. Furthermore, the LFP battery pack was fully charged after achieving a 50% State of Charge (SOC) under every current-mode condition, providing reliable outputs under the loading conditions. It is also stated that the state of health of the lithium ferrous phosphate is significantly higher at 92% during the entire investigation, which reflects the good thermal stability of the LFP battery pack for temperature variations from 26 °C to 31 °C. Finally, it is concluded that the LFP could be one of the most favourable energy storage systems due to its longer lifespan and its great affordability in automotive applications. Full article

The Energy Performance of Buildings Directive (EPBD) has set a target to achieve carbon-neutral building stock and generate 80% of its electricity from renewable sources by 2050. While Model Predictive Control (MPC) can contribute significantly to energy flexibility in buildings, its remote implementation remains relatively unexplored, especially in the residential sector. The purpose of this research is to demonstrate the reliability, robustness, and computational efficiency of a cloud-based application of an MPC called Smart Energy Management (SEM) on a multi-family residential building. The SEM was tested on a virtual building model in TRNSYS using an open-source distributed event streaming platform for data exchange and synchronization. Simplified models for thermal behavior prediction, including an R3C3 model of the building, were developed in C++. The SEM was evaluated in eight scenarios with varying weather conditions, optimization criteria, and runtime periods. The results demonstrate that the SEM maintains stability and robustness over a 2-week period with a 15-minute planning resolution while ensuring thermal comfort. The C++ implementation of the optimization algorithm enables SEM deployment on low-spec servers, supporting cost-effective applications in real buildings with minimal intervention. Full article

In this paper, the magnitude of the temperature and stress variability of dynamic voltage and frequency scaling (DVFS) designs is analyzed, and their impact on the bias temperature instability (BTI) degradation and lifetime of DVFS designs is assessed. For this purpose, a design-time evaluation framework for BTI degradation was developed, which considered the statistical workload and die temperature profiles of DVFS operating modes. The performed analysis showed that, together with high stress variability, DVFS designs exhibited even higher temperature variability, depending on the workload and utilized operating modes, and the impact of temperature variability on lifetime could be up to 2× higher than that due to stress. In order to account for temperature variability on aging detrimental effects, a thermal management run-time system is proposed that honors the desired lifetime constraints by properly selecting temperature constraints that govern the utilized operating modes. The proposed run-time system was applied on the largest benchmark circuit from the IWLS 2005 suite, Ethernet circuit, synthesized with the 32 nm CMOS technology. The proposed system was verified to obtain lifetime and performance estimation and the trade-off with up to 35.8% and 26.3% higher accuracy, respectively, when compared to a system that ignores temperature variability and accounts for average temperature only. The proposed framework can be suitably utilized for tuning run-time throttling policies of low-power designs, thus allowing designers to optimize lifetime–performance trade-offs, depending on the requirements mandated by specific applications and operating environments. Full article

Die-stacking technology is expanding the space diversity of on-chip communications by leveraging through-silicon-via (TSV) integration and wafer bonding. The 3D network-on-chip (NoC), a combination of die-stacking technology and systematic on-chip communication infrastructure, suffers from increased thermal density and unbalanced heat dissipation across multi-stacked layers, significantly affecting chip performance and reliability. Recent studies have focused on runtime thermal management (RTM) techniques for improving the heat distribution balance, but performance degradations, owing to RTM mechanisms and unbalanced inter-layer traffic distributions, remain unresolved. In this study, we present a Q-function-based traffic- and thermal-aware adaptive routing algorithm, utilizing a reinforcement machine learning technique that gradually incorporates updated information into an RTM-based 3D NoC routing path. The proposed algorithm initially collects deadlock-free directions, based on the RTM and topology information. Subsequently, Q-learning-based decision making (through the learning of regional traffic information) is deployed for performance improvement with more balanced inter-layer traffic. The simulation results show that the proposed routing algorithm can improve throughput by 14.0%–28.2%, with a 24.9% more balanced inter-layer traffic load and a 30.6% more distributed inter-layer thermal dissipation on average, compared with those obtained in previous studies of a 3D NoC with an 8 $\times$ 8 $\times$ 4 mesh topology. Full article

Dynamic thermal management (DTM) mechanisms utilize embedded thermal sensors to collect fine-grained temperature information for monitoring the real-time thermal behavior of multi-core processors. However, embedded thermal sensors are very susceptible to a variety of sources of noise, including environmental uncertainty and process variation. This causes the discrepancies between actual temperatures and those observed by on-chip thermal sensors, which seriously affect the efficiency of DTM. In this paper, a smoothing filter-based Kalman prediction technique is proposed to accurately estimate the temperatures from noisy sensor readings. For the multi-sensor estimation scenario, the spatial correlations among different sensor locations are exploited. On this basis, a multi-sensor synergistic calibration algorithm (known as MSSCA) is proposed to improve the simultaneous prediction accuracy of multiple sensors. Moreover, an infrared imaging-based temperature measurement technique is also proposed to capture the thermal traces of an advanced micro devices (AMD) quad-core processor in real time. The acquired real temperature data are used to evaluate our prediction performance. Simulation shows that the proposed synergistic calibration scheme can reduce the root-mean-square error (RMSE) by 1.2 ${}^{\circ }$ C and increase the signal-to-noise ratio (SNR) by 15.8 dB (with a very small average runtime overhead) compared with assuming the thermal sensor readings to be ideal. Additionally, the average false alarm rate (FAR) of the corrected sensor temperature readings can be reduced by 28.6%. These results clearly demonstrate that if our approach is used to perform temperature estimation, the response mechanisms of DTM can be triggered to adjust the voltages, frequencies, and cooling fan speeds at more appropriate times. Full article

The hybrid-power gas engine-driven heat pump (HPGHP) combines hybrid power technology with a gas engine heat pump. The engine in the power system is capable of operating constantly with high thermal efficiency and low emissions during different operating modes. In this paper, the mathematical models of various components is established, including the engine thermal efficiency map and the motor efficiency map. The comprehensive charging/discharging efficiency model and energy management optimization strategy model which is proposed to maximize the efficiency of instantaneous HPGHP system are established. Then, different charging/discharging torque limits are obtained. Finally, a novel gas engine economical zone control strategy which combined with the SOC of battery in real time is put forward. The main operating parameters of HPGHP system under energy management are simulated by Matlab/Simulink and validated by experimental data, such as engine and motor operating torque, fuel consumption rate and comprehensive efficiency, etc. The results show that during 3600 s’ run-time, the SOC value of battery packs varies between 0.58 and 0.705, the fuel consumption rate reaches minimum values of approximately 291.3 g/(kW h) when the compressor speed is nearly 1550 rpm in mode D, the engine thermal efficiency and comprehensive efficiency reach maximum values of approximately 0.2727 and 0.2648 when the compressor speed is 1575 rpm and 1475 rpm, respectively, in mode D. In general, the motor efficiency can be maintained above 0.85 in either mode. Full article