In this paper, driving strategy optimization for a track is proposed for an energy efficient battery electric vehicle dedicated to the Shell Eco-marathon. A measurement-based mathematical vehicle ...model was developed to simulate the behavior of the vehicle. The model contains complicated elements such as the vehicle’s cornering resistance and the efficiency field of the entire powertrain. The validation of the model was presented by using the collected telemetry data from the 2019 Shell Eco-marathon competition in London (UK). The evaluation of applicable powertrains was carried out before the driving strategy optimization. The optimal acceleration curve for each investigated powertrain was defined. Using the proper powertrain is a crucial part of energy efficiency, as the drive has the most significant energy demand among all components. Two tracks with different characteristics were analyzed to show the efficiency of the proposed optimization method. The optimization results are compared to the reference method from the literature. The results of this study provide an applicable vehicle modelling methodology with efficient optimization framework, which demonstrates 5.5% improvement in energy consumption compared to the reference optimization theory.
Vertical farming (VF) has been proposed as an approach to decrease the land required for growing agricultural products. This technique consists of growing produce in vertical orientation within a ...controlled environment. However, one of the most significant barriers for its implementation is the uncertain economic feasibility, derived from the elevated consumption of energy and the high investment costs. A strategy to enhance VF efficiency proposes its integration with municipal infrastructure, thus establishing closed-loop systems where VF seizes organic waste, manure, CO2, and excess energy from productive plants and local power stations. Because of the economic uncertainty of its development, the optimal synthesis of such a closed-loop system (i.e., the selection and specification of its components, and their connections) is of utmost importance for the implementation of this strategy. The difficulty of the synthesis task arises from the combinatorial nature of the problem and the variability of the resources and market conditions in time. This work employs a graph-theoretic approach for the synthesis of a closed-loop system of VF considering the variability of the resources during multiple periods of operation. The proposed method relies on the P-graph framework which permits the identification of the n-best alternatives for the system’s design, employing the properties of the problem’s structure to enhance the effectiveness of the solution procedure. Consequently, the most cost-effective systems are identified together with their policy of operation for the different periods. This method constitutes a powerful tool for the assessment of systems for VF integration that enhance the sustainability of agricultural activity.
In this paper, the energy saving effect of optimized driving strategy is presented and compared to human driving strategy. The driving strategy of a one-seated experimental electric vehicle is ...investigated and optimized in this study, where the objective function of optimization is the minimization of the consumed energy. Measurement-based vehicle model is used during the optimization process. The initialization and constraints of optimization are set up by analyzing the acquired vehicle data of the driver. The analyzation is done using a transform algorithm, making the initialization of optimization automated. Genetic algorithm is used with mixed initial population acquired from measured driving data and from creation function. Using this hybrid initial population helped to decrease the time of optimization. The resulted velocity profile of the optimized driving strategy was used in field test measurements, where 4.28 % energy savings was achieved compared to the results prior to optimization.
We propose a process graph (P-graph) approach to develop ecosystem networks from knowledge of the properties of the component species. Originally developed as a process engineering tool for designing ...industrial plants, the P-graph framework has key advantages over conventional ecological network analysis techniques based on input-output models. A P-graph is a bipartite graph consisting of two types of nodes, which we propose to represent components of an ecosystem. Compartments within ecosystems (e.g., organism species) are represented by one class of nodes, while the roles or functions that they play relative to other compartments are represented by a second class of nodes. This bipartite graph representation enables a powerful, unambiguous representation of relationships among ecosystem compartments, which can come in tangible (e.g., mass flow in predation) or intangible form (e.g., symbiosis). For example, within a P-graph, the distinct roles of bees as pollinators for some plants and as prey for some animals can be explicitly represented, which would not otherwise be possible using conventional ecological network analysis. After a discussion of the mapping of ecosystems into P-graph, we also discuss how this framework can be used to guide understanding of complex networks that exist in nature. Two component algorithms of P-graph, namely maximal structure generation (MSG) and solution structure generation (SSG), are shown to be particularly useful for ecological network analysis. These algorithms enable candidate ecosystem networks to be deduced based on current scientific knowledge on the individual ecosystem components. This method can be used to determine the (a) effects of loss of specific ecosystem compartments due to extinction, (b) potential efficacy of ecosystem reconstruction efforts, and (c) maximum sustainable exploitation of human ecosystem services by humans. We illustrate the use of P-graph for the analysis of ecosystem compartment loss using a small-scale stylized case study, and further propose a new criticality index that can be easily derived from SSG results.
Separation of azeotropic mixtures receives special attention for their impact on various significant industrial processes. Because of the non-ideal behaviour of these mixtures, it is impossible to ...separate them by conventional distillation. Instead of a single distillation unit, a system of multiple operations is to be employed. Heterogeneous azeotropic distillation (HAD) is an example of this kind of systems, where entrainers are applied to modify the behaviour of the mixture. The selection of the best separation system is a key objective during the synthesis of the process network. However, synthesis of HAD is especially difficult because of the complex interaction between its continuous and discrete features. Therefore, traditional separation network synthesis tools are incapable of solving this problem. In this work, the properties of the ternary vapor-liquid-liquid equilibrium diagram are exploited for systematically identifying plausible operating units that perform the separation of the azeotrope. Subsequently, energy consumption of the entire network is estimated through rigorous simulation. The P-graph framework is employed to represent the system’s structure. Additionally, its combinatorial algorithms generate a rigorous superstructure for the synthesis problem, and the set of n-best designs that minimize energy consumption. The method is illustrated by synthesizing the dehydration of furfural through HAD. The results demonstrate that it constitutes a valuable tool for the designer by being effective in the systematic identification and assessment of HAD alternatives.
The usage of regenerative braking highly improves the overall energy efficiency of electric vehicles. In this paper, the model-based optimization of the torque profile is determined in the ...regenerative braking process of a lightweight electric vehicle. For the optimization, measurement-based vehicle model was used, where the extended powertrain model was set up, including the regenerative operation. The whole model was elaborated in MATLAB Simulink environment, where genetic algorithm (GA) was applied for the optimization. The resulted optimized braking curve was applied to control the experimental vehicle and field test were made to validate the optimization results. The results of the presented work can be directly used to further improve the drive cycle efficiency of the urban electric vehicles. The application of optimized driving strategies, including regenerative braking, could contribute to further energy and pollution reduction in urban transportation.
The circular economy has become one of the most popular topics in worldwide sustainability research. The imperious necessity of reducing resource consumption and decreasing waste generation has led ...to reincorporating materials at the end-of-life (EoL) stage into the productive chain. Nonetheless, the presence of hazardous substances in the EoL stage materials poses a significant challenge for the transition toward the production model. The adequate transformation of these materials into feedstocks requires their correct allocation into recovery plants and final destinations. Such an allocation can be decided by resorting to optimisation by generating the best alternative networks, from where the stakeholders may decide the most suitable recycling scheme. In this work, a graph-theoretic approach is introduced to identify the best alternatives to reincorporate industrial EoL chemicals into the productive chain. This contribution presents the initial approach to this problem, demonstrated through a case study considering the data reported on the public-access release inventory data for n-hexane. Different recycling routes are proposed for the case study by optimising the total treatment cost, and their advantages and disadvantages are discussed; moreover, their efficiency concerning the circular economy is measured by comparing the amount of recovered chemicals. By generating plausible recycling alternatives, this work contributes positively to analysing potential alternatives for circular economy and resource conservation in industry.
In this paper, determination of optimized regenerative braking-torque function and application in energy efficient driving strategies is presented. The study investigates a lightweight electric ...vehicle developed for the Shell Eco-Marathon. The measurement-based simulation model was implemented in the MATLAB/Simulink environment and used to establish the optimization. The optimization of braking-torque function was performed to maximize the recuperated energy. The determined braking-torque function was applied in a driving strategy optimization framework. The extended driving strategy optimization model is suitable for energy consumption minimization in a designated track. The driving strategy optimization was created for the TT Circuit Assen, where the 2022 Shell Eco-Marathon competition was hosted. The extended optimization resulted in a 2.97% improvement in energy consumption when compared to the result previously achieved, which shows the feasibility of the proposed methodology and optimization model.
Energy saving, global warming and greenhouse gas emissions have become major technological, societal, and political issues. Being closely related to energy supply, they are of a strategic importance. ...Various conferences are being organised for providing international venues for closer cooperation among researchers. The series of conferences “Process Integration, Modelling and Optimisation for Energy Saving and Pollution Reduction” (PRES) play a pioneering role in contributing to the solution of the related problems through presenting new methodologies and initiating cooperation among participants that often result in international projects. The PRES conferences have been dedicated both to the theoretical and to the practical aspects of energy saving and pollution reduction. The PRES series, established thirteen years ago, was originally dedicated to energy integration and improving thermal efficiency. Following the new challenges and priorities, the scope of the conferences has been extended to include all energy and pollution prevention related issues. This contribution focuses on the period covered by PRES, reviewing at least some major presentations contributing to the development of process integration and optimisation tools for energy saving and pollution reduction. The development of the mathematical models has been covered as well, since it is closely related to the area.
The energy consumption and CO2 emission of urban vehicles are highly dependent on their operation. Vehicle models can be used for optimizing driving strategy for emission reduction. This paper ...proposes a novel vehicle model of a one-seat electric vehicle dedicated for Shell Eco-marathon (SEM), the most famous and largest race of energy efficient vehicles. The available vehicle dynamical formulas cannot be directly used to describe the characteristics of lightweight vehicles. In the current work, a novel grey-box vehicle model has been introduced, based on measurement scenarios. The whole model has been elaborated in MATLAB Simulink environment, where individual subassemblies were defined for driving resistance model, powertrain model, and the racetrack characteristics. The resistance force model manages the forces in straight line moving and also takes the effect of cornering into account. Based on test bench measurements the complete efficiency map of the drivetrain was created and implemented into the vehicle model. The presented vehicle model is suitable for driving strategy optimization. By optimizing this model, 7.1 % energy savings have been achieved compared to best human driven lap. Driving strategy optimization will be essential, especially for autonomous vehicles, expressing the importance of the presented results in the future.
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