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In response to the need of reducing fossil fuel dependence and environmental impacts for ground transportation, electric vehicles (EVs) powered by lithium-ion batteries (LIBs) are being intensively researched and they have placed on the forefront as alternative vehicles. The state of charge (SOC) is one of the most important states of LIBs that is monitored online. However, the model-based method state of charge estimation requires an accurate Open circuit voltage (OCV), which is an important characteristic parameter of lithium-ion batteries, that is used to estimate battery state of charge (SOC). Therefore, accurate OCV modeling is a great significance for lithium-ion battery management. The polynomial OCV model uses the polynomial function to establish the relationship between OCV and SOC mapping. In this paper,8th degree polynomial fitting curve is considered and the genetic algorithm optimization method is proposed for estimating the parameters. The results show that the root mean square error can be decreased to 0.002. However, the best fitting OCV-SOC curve can increase the accuracy of the model and improve the accuracy of battery state estimation.

In response to the need of reducing fossil fuel dependence and environmental impacts for ground transportation, electric vehicles (EVs) powered by lithium-ion batteries (LIBs) are being intensively researched and they have placed on the forefront as alternative vehicles. The state of charge (SOC) is one of the most important states of LIBs that is monitored online. However, the model-based method state of charge estimation requires an accurate Open circuit voltage (OCV), which is an important characteristic parameter of lithium-ion batteries, that is used to estimate battery state of charge (SOC). Therefore, accurate OCV modeling is a great significance for lithium-ion battery management. The polynomial OCV model uses the polynomial function to establish the relationship between OCV and SOC mapping. In this paper,8th degree polynomial fitting curve is considered and the genetic algorithm optimization method is proposed for estimating the parameters. The results show that the root mean square error can be decreased to 0.002. However, the best fitting OCV-SOC curve can increase the accuracy of the model and improve the accuracy of battery state estimation.

Abstract Solar dryers are traditional devices used for drying various products. Different indirect solar dryer (ISD) geometries were theoretically examined using computational fluid dynamics (CFD). This paper presents a numerical investigation of two indirect solar dryers using CFD simulation, comparing the velocity and thermal performance of dryers with smooth and corrugated absorber plates. The temperature values obtained by numerical simulations were compared to the experimental measurements and found a maximum variation difference of 1.26%. The maximum velocity in the solar air collector (SAC) and the value of average temperature at the SAC outlet were found to be 0.58 m/s and 336 K for the smooth absorber ISD, and 0.77 m/s and 350 K for the corrugated absorber ISD. It was observed that the corrugated absorber plate exhibited superior thermal performance and a higher maximum velocity compared to the smooth absorber plate. Within the cabinet, a uniform temperature profile was observed, particularly for the corrugated case. V-shaped absorber plates offer higher heat transfer rates, increased turbulence, and greater surface area for heat transfer, making them more efficient for drying processes compared to smooth absorber plates. Therefore, corrugated absorber plates in solar air collectors are a more efficient option than using smooth absorber plates.

Abstract Solar dryers are traditional devices used for drying various products. Different indirect solar dryer (ISD) geometries were theoretically examined using computational fluid dynamics (CFD). This paper presents a numerical investigation of two indirect solar dryers using CFD simulation, comparing the velocity and thermal performance of dryers with smooth and corrugated absorber plates. The temperature values obtained by numerical simulations were compared to the experimental measurements and found a maximum variation difference of 1.26%. The maximum velocity in the solar air collector (SAC) and the value of average temperature at the SAC outlet were found to be 0.58 m/s and 336 K for the smooth absorber ISD, and 0.77 m/s and 350 K for the corrugated absorber ISD. It was observed that the corrugated absorber plate exhibited superior thermal performance and a higher maximum velocity compared to the smooth absorber plate. Within the cabinet, a uniform temperature profile was observed, particularly for the corrugated case. V-shaped absorber plates offer higher heat transfer rates, increased turbulence, and greater surface area for heat transfer, making them more efficient for drying processes compared to smooth absorber plates. Therefore, corrugated absorber plates in solar air collectors are a more efficient option than using smooth absorber plates.

This paper models the retrofitting of traditional rural dwellings in Uganda by introducing passive cooling and daylighting measures. With climate change, outdoor temperatures in parts of the Global South will become unbearable. Communities will be forced to spend more time indoors and thus require further cooling and daytime lighting for thermal and visual comfort. Passive measures can address this while also attenuating energy consumption and diversifying energy use. For the first time, these measures are combined and modelled for those purposes in a rural developing country context where there is limited access to electricity. Using SketchUp and EnergyPlus™, we simulate 15 scenarios of passive cooling and daylighting measures for two baseline scenarios: electrified mud hut and semi-permanent dwelling. Those are also feasible scenarios for similar greenfield unelectrified architectures. The results from the simulated scenarios on electrified dwellings include energy savings, and from these, payback periods are calculated. Two cost-effective scenarios are identified. For mud huts, combining shading (through vegetation) with a centralised fibre optic solar concentrator enables an annual reduction of 31 % of energy consumption. For semi-permanent dwellings, painting rooftops white combined with the integration of solar water light bulbs reduces energy consumption by 47 %. In terms of payback periods for the selected scenarios, the former is four years and nine months, while the latter is two months.

This paper models the retrofitting of traditional rural dwellings in Uganda by introducing passive cooling and daylighting measures. With climate change, outdoor temperatures in parts of the Global South will become unbearable. Communities will be forced to spend more time indoors and thus require further cooling and daytime lighting for thermal and visual comfort. Passive measures can address this while also attenuating energy consumption and diversifying energy use. For the first time, these measures are combined and modelled for those purposes in a rural developing country context where there is limited access to electricity. Using SketchUp and EnergyPlus™, we simulate 15 scenarios of passive cooling and daylighting measures for two baseline scenarios: electrified mud hut and semi-permanent dwelling. Those are also feasible scenarios for similar greenfield unelectrified architectures. The results from the simulated scenarios on electrified dwellings include energy savings, and from these, payback periods are calculated. Two cost-effective scenarios are identified. For mud huts, combining shading (through vegetation) with a centralised fibre optic solar concentrator enables an annual reduction of 31 % of energy consumption. For semi-permanent dwellings, painting rooftops white combined with the integration of solar water light bulbs reduces energy consumption by 47 %. In terms of payback periods for the selected scenarios, the former is four years and nine months, while the latter is two months.

The field of energy forecasting has attracted many researchers from different fields (e.g., meteorology, data sciences, mechanical or electrical engineering) over the last decade. Solar forecasting is a fast-growing sub-domain of energy forecasting. Despite several previous attempts, the methods and measures used for verification of deterministic (also known as single-valued or point) solar forecasts are still far from being standardized, making forecast analysis and comparison difficult. To analyze and compare solar forecasts, the well-established Murphy-Winkler framework for distribution-oriented forecast verification is recommended as a standard practice. This framework examines aspects of forecast quality, such as reliability, resolution, association, or discrimination, and analyzes the joint distribution of forecasts and observations, which contains all time-independent information relevant to verification. To verify forecasts, one can use any graphical display or mathematical/statistical measure to provide insights and summarize the aspects of forecast quality. The majority of graphical methods and accuracy measures known to solar forecasters are specific methods under this general framework. Additionally, measuring the overall skillfulness of forecasters is also of general interest. The use of the root mean square error (RMSE) skill score based on the optimal convex combination of climatology and persistence methods is highly recommended. By standardizing the accuracy measure and reference forecasting method, the RMSE skill score allows-with appropriate caveats-comparison of forecasts made using different models, across different locations and time periods.

The field of energy forecasting has attracted many researchers from different fields (e.g., meteorology, data sciences, mechanical or electrical engineering) over the last decade. Solar forecasting is a fast-growing sub-domain of energy forecasting. Despite several previous attempts, the methods and measures used for verification of deterministic (also known as single-valued or point) solar forecasts are still far from being standardized, making forecast analysis and comparison difficult. To analyze and compare solar forecasts, the well-established Murphy-Winkler framework for distribution-oriented forecast verification is recommended as a standard practice. This framework examines aspects of forecast quality, such as reliability, resolution, association, or discrimination, and analyzes the joint distribution of forecasts and observations, which contains all time-independent information relevant to verification. To verify forecasts, one can use any graphical display or mathematical/statistical measure to provide insights and summarize the aspects of forecast quality. The majority of graphical methods and accuracy measures known to solar forecasters are specific methods under this general framework. Additionally, measuring the overall skillfulness of forecasters is also of general interest. The use of the root mean square error (RMSE) skill score based on the optimal convex combination of climatology and persistence methods is highly recommended. By standardizing the accuracy measure and reference forecasting method, the RMSE skill score allows-with appropriate caveats-comparison of forecasts made using different models, across different locations and time periods.