Criteria for Solar Car Optimized Route Estimation

Solar cars are known for their energy efficiency, and different races are designed to measure their performance under certain conditions. For these races, in addition to an efficient vehicle, a competition strategy is required to define the optimal speed, with the objective of finishing the race in the shortest possible time using the energy available. Two heuristic optimization methods are implemented to solve this problem, a convergence and performance comparison of both methods is presented. A computational model of the race is developed, including energy input, consumption and storage systems. Based on this model, the different optimization methods are tested on the optimization of the World Solar Challenge 2015 race strategy under two different environmental conditions. A suitable method for solar car racing strategy is developed with the vehicle specifications taken as an independent input to permit the simulation of different solar or electric vehicles.

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“…The route planning and selection [9] is excluded from the race strategy considering that solar car races have a strictly defined path.…”

Section: Solar Car Racing Strategymentioning

confidence: 99%

Heuristic Optimization for the Energy Management and Race Strategy of a Solar Car

2017

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“…First, forecasting the solar panel energy outputs in a specific region can assist investors in properly building and installing solar plants with an accurate capacity to enable optimal management of the energy system (Geetha et al, 2022). In addition, with the development of solar energy, enhanced cars, drones, and various vehicles, the solar car optimized router estimation (SCORE) that utilizes solar energy to pick up the route with the maximum solar energy absorption route is under intensive evaluations (Hasicic et al, 2017). The SCORE requires forecasting solar energy in real time based on all available solar irradiance, weather, and public transportation data into the model (Hasicic et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…In addition, with the development of solar energy, enhanced cars, drones, and various vehicles, the solar car optimized router estimation (SCORE) that utilizes solar energy to pick up the route with the maximum solar energy absorption route is under intensive evaluations (Hasicic et al, 2017). The SCORE requires forecasting solar energy in real time based on all available solar irradiance, weather, and public transportation data into the model (Hasicic et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network modeling of solar photovoltaic panel energy output

Zeng

2024

10.5267/j.jfs

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Solar panel energy output is an essential parameter for the design and operation of renewable energy systems. Previously, little was known about the precise relationship between the energy outputs of solar panels with various meteorological, radiometric, and weather conditions in the southern California region. Without precise modeling or prediction systems, solar energy can potentially be wasted due to grid energy fluctuation. Thus, it is intended to use an artificial neural network (ANN) to develop solar panel energy output prediction model with a high degree of accuracy. A self-developed feedforward ANN model utilizing the Rectified linear unit (ReLu) activation function was used in the present study. Meteorological, weather, and sun irradiation data collected throughout the last year from a residential location have been used to train the models. The model’s performance was identified based on the minimum mean absolute error (MAE) and root mean square error (RMSE) and maximum linear correlation coefficient (R2). Further, the present self-developed ANN model was consistent with other solar energy experimental results and theoretical analysis. The developed ANN model using the Python programming language achieved a high R2 of more than 85% which ascertains the accuracy and suitability of the model to predict the daily solar energy output in local southern California area. This ANN modeling approach can be extended to many other applications such as SCORE, commercial, and residential building design.

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“…The first is navigation technology that analyzes the optimal driving route to maximize photovoltaic (PV) power generation and minimize energy consumption while driving. Recent research includes speed planning on road segments [29], solar PV energy mapping [30], and route planning considering solar insolation, shadow effect, road slope, travel time, traffic flow, energy consumption, and solar power generation [31][32][33][34]. The second category is technology to analyze optimal parking lots and parking spaces to maximize PV power generation when parked.…”

Section: Introductionmentioning

confidence: 99%

An Experimental Study on Performance Evaluation of Shading Matrix to Select Optimal Parking Space for Solar-Powered Electric Vehicles

Baek

Choi

2022

Sustainability

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This study evaluated the applicability of a shading matrix generated using hemispherical fisheye images to select the optimal parking space to maximize the photovoltaic (PV) power generation of solar electric vehicles. The study aims to identify the maximum power point (MPP) of a PV module in a parking space using current–voltage measuring equipment to determine whether the shading matrix reflects any shadow phenomenon. The experiment was conducted on a parking lot at Daeyeon Campus. The fisheye images were taken in seven parking spaces. The shading matrices were generated by tracking the position of the sun over 12 months and 24 h periods. A 50 W PV module was installed on the roof of the vehicle and the MPP was measured in five experiments (9 a.m., 11 a.m., 13 p.m., 15 p.m., and 17 p.m.) in seven parking spaces. In parking spaces with a shading factor of 0, the PV modules were not shaded at all during the experiment, and the average MPPs were 34, 43, 37, and 21 W at 9 a.m., 11 a.m., 13 p.m., and 15 p.m., respectively. The shading matrix calculated using the fisheye images accurately reflected the shadow phenomenon that decreases PV power generation.

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Criteria for Solar Car Optimized Route Estimation

Cited by 16 publications

References 34 publications

Heuristic Optimization for the Energy Management and Race Strategy of a Solar Car

Heuristic Optimization for the Energy Management and Race Strategy of a Solar Car

Artificial neural network modeling of solar photovoltaic panel energy output

An Experimental Study on Performance Evaluation of Shading Matrix to Select Optimal Parking Space for Solar-Powered Electric Vehicles

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