Why the development of internal combustion engines is still necessary to fight against global climate change from the perspective of transportation

[EN] Internal combustion engines (ICE) are the main propulsion systems in road transport. In mid-2017, Serrano referred to the impossibility of replacing them as the power plant in most vehicles. Nowadays, this statement is true even when considering the best growth scenario for all-electric and hybrid vehicles. The arguments supporting this position consider the growing demand for transport, the strong development of cleaner and more efficient ICEs, the availability of fossil fuels, and the high energy density of said conventional fuels. Overall, there seems to be strong arguments to support the medium-long-term viability of ICEs as the predominant power plant for road transport applications. However, the situation has changed dramatically in the last few years. The media and other market players are claiming the death of ICEs in the mid-term. Politicians from several G7 countries, such as France, Spain, and the United Kingdom, have announced the prohibition of ICEs in their markets, in some cases, as early as 2040. Large cities, such London, Paris, Madrid, and Berlin, are also considering severe limits to ICE-powered vehicles. What is the analysis that can be made from this new situation?Serrano, J.; Novella Rosa, R.; Piqueras, P. (2019). Why the development of internal combustion engines is still necessary to fight against global climate change from the perspective of transportation. Applied Sciences. 9(21):1-11. https://doi.org/10.3390/app9214597S111921Serrano, J. (2017). Imagining the Future of the Internal Combustion Engine for Ground Transport in the Current Context. Applied Sciences, 7(10), 1001. doi:10.3390/app7101001Ding, Y., Sui, C., & Li, J. (2018). An Experimental Investigation into Combustion Fitting in a Direct Injection Marine Diesel Engine. 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Safe reinforcement learning with self-improving hard constraints for multi-energy management systems

Safe reinforcement learning (RL) with hard constraint guarantees is a promising optimal control direction for multi-energy management systems. It only requires the environment-specific constraint functions itself a prior and not a complete model (i.e. plant, disturbance and noise models, and prediction models for states not included in the plant model - e.g. demand, weather, and price forecasts). The project-specific upfront and ongoing engineering efforts are therefore still reduced, better representations of the underlying system dynamics can still be learned and modeling bias is kept to a minimum (no model-based objective function). However, even the constraint functions alone are not always trivial to accurately provide in advance (e.g. an energy balance constraint requires the detailed determination of all energy inputs and outputs), leading to potentially unsafe behavior. In this paper, we present two novel advancements: (I) combining the Optlayer and SafeFallback method, named OptLayerPolicy, to increase the initial utility while keeping a high sample efficiency. (II) introducing self-improving hard constraints, to increase the accuracy of the constraint functions as more data becomes available so that better policies can be learned. Both advancements keep the constraint formulation decoupled from the RL formulation, so that new (presumably better) RL algorithms can act as drop-in replacements. We have shown that, in a simulated multi-energy system case study, the initial utility is increased to 92.4% (OptLayerPolicy) compared to 86.1% (OptLayer) and that the policy after training is increased to 104.9% (GreyOptLayerPolicy) compared to 103.4% (OptLayer) - all relative to a vanilla RL benchmark. While introducing surrogate functions into the optimization problem requires special attention, we do conclude that the newly presented GreyOptLayerPolicy method is the most advantageous.Comment: 4579 words. arXiv admin note: text overlap with arXiv:2207.0383

Environmental impacts of hybrid, plug-in hybrid, and battery electric vehicles—what can we learn from life cycle assessment?

PurposeThe purpose of this review article is to investigate the usefulness of different types of life cycle assessment (LCA) studies of electrified vehicles to provide robust and relevant stakeholder information. It presents synthesized conclusions based on 79 papers. Another objective is to search for explanations to divergence and “complexity” of results found by other overviewing papers in the research field, and to compile methodological learnings. The hypothesis was that such divergence could be explained by differences in goal and scope definitions of the reviewed LCA studies.MethodsThe review has set special attention to the goal and scope formulation of all included studies. First, completeness and clarity have been assessed in view of the ISO standard’s (ISO 2006a, b) recommendation for goal definition. Secondly, studies have been categorized based on technical and methodological scope, and searched for coherent conclusions.Results and discussionComprehensive goal formulation according to the ISO standard (ISO 2006a, b) is absent in most reviewed studies. Few give any account of the time scope, indicating the temporal validity of results and conclusions. Furthermore, most studies focus on today’s electric vehicle technology, which is under strong development. Consequently, there is a lack of future time perspective, e.g., to advances in material processing, manufacturing of parts, and changes in electricity production. Nevertheless, robust assessment conclusions may still be identified. Most obvious is that electricity production is the main cause of environmental impact for externally chargeable vehicles. If, and only if, the charging electricity has very low emissions of fossil carbon, electric vehicles can reach their full potential in mitigating global warming. Consequently, it is surprising that almost no studies make this stipulation a main conclusion and try to convey it as a clear message to relevant stakeholders. Also, obtaining resources can be observed as a key area for future research. In mining, leakage of toxic substances from mine tailings has been highlighted. Efficient recycling, which is often assumed in LCA studies of electrified vehicles, may reduce demand for virgin resources and production energy. However, its realization remains a future challenge.ConclusionsLCA studies with clearly stated purposes and time scope are key to stakeholder lessons and guidance. It is also necessary for quality assurance. LCA practitioners studying hybrid and electric vehicles are strongly recommended to provide comprehensive and clear goal and scope formulation in line with the ISO standard (ISO 2006a, b)

Electricity generation in LCA of electric vehicles: A review

Life Cycle assessments (LCAs) on electric mobility are providing a plethora of diverging results. 44 articles, published from 2008 to 2018 have been investigated in this review, in order to find the extent and the reason behind this deviation. The first hurdle can be found in the goal definition, followed by the modelling choice, as both are generally incomplete and inconsistent. These gaps influence the choices made in the Life Cycle Inventory (LCI) stage, particularly in regards to the selection of the electricity mix. A statistical regression is made with results available in the literature. It emerges that, despite the wide-ranging scopes and the numerous variables present in the assessments, the electricity mix's carbon intensity can explain 70% of the variability of the results. This encourages a shared framework to drive practitioners in the execution of the assessment and policy makers in the interpretation of the results

TreeC: a method to generate interpretable energy management systems using a metaheuristic algorithm

Energy management systems (EMS) have classically been implemented based on rule-based control (RBC) and model predictive control (MPC) methods. Recent research are investigating reinforcement learning (RL) as a new promising approach. This paper introduces TreeC, a machine learning method that uses the metaheuristic algorithm covariance matrix adaptation evolution strategy (CMA-ES) to generate an interpretable EMS modeled as a decision tree. This method learns the decision strategy of the EMS based on historical data contrary to RBC and MPC approaches that are typically considered as non adaptive solutions. The decision strategy of the EMS is modeled as a decision tree and is thus interpretable contrary to RL which mainly uses black-box models (e.g. neural networks). The TreeC method is compared to RBC, MPC and RL strategies in two study cases taken from literature: (1) an electric grid case and (2) a household heating case. The results show that TreeC obtains close performances than MPC with perfect forecast in both cases and obtains similar performances to RL in the electrical grid case and outperforms RL in the household heating case. TreeC demonstrates a performant application of machine learning for energy management systems that is also fully interpretable

Eco-Efficiency of a Lithium-Ion Battery for Electric Vehicles: Influence of Manufacturing Country and Commodity Prices on GHG Emissions and Costs

Lithium-ion battery packs inside electric vehicles represents a high share of the final price. Nevertheless, with technology advances and the growth of the market, the price of the battery is getting more competitive. The greenhouse gas emissions and the battery cost have been studied previously, but coherent boundaries between environmental and economic assessments are needed to assess the eco-efficiency of batteries. In this research, a detailed study is presented, providing an environmental and economic assessment of the manufacturing of one specific lithium-ion battery chemistry. The relevance of parameters is pointed out, including the manufacturing place, the production volume, the commodity prices, and the energy density. The inventory is obtained by dismantling commercial cells. The correlation between the battery cost and the commodity price is much lower than the correlation between the battery cost and the production volume. The developed life cycle assessment concludes that the electricity mix that is used to power the battery factory is a key parameter for the impact of the battery manufacturing on climate change. To improve the battery manufacturing eco-efficiency, a high production capacity and an electricity mix with low carbon intensity are suggested. Optimizing the process by reducing the electricity consumption during the manufacturing is also suggested, and combined with higher pack energy density, the impact on climate change of the pack manufacturing is as low as 39.5 kg CO2 eq/kWh. Document type: Articl

Safe reinforcement learning for multi-energy management systems with known constraint functions

Reinforcement learning (RL) is a promising optimal control technique for multi-energy management systems. It does not require a model a priori - reducing the upfront and ongoing project-specific engineering effort and is capable of learning better representations of the underlying system dynamics. However, vanilla RL does not provide constraint satisfaction guarantees — resulting in various potentially unsafe interactions within its environment. In this paper, we present two novel online model-free safe RL methods, namely SafeFallback and GiveSafe, where the safety constraint formulation is decoupled from the RL formulation. These provide hard-constraint satisfaction guarantees both during training and deployment of the (near) optimal policy. This is without the need of solving a mathematical program, resulting in less computational power requirements and more flexible constraint function formulations. In a simulated multi-energy systems case study we have shown that both methods start with a significantly higher utility compared to a vanilla RL benchmark and Optlayer benchmark (94,6% and 82,8% compared to 35,5% and 77,8%) and that the proposed SafeFallback method even can outperform the vanilla RL benchmark (102,9% to 100%). We conclude that both methods are viably safety constraint handling techniques applicable beyond RL, as demonstrated with random policies while still providing hard-constraint guarantees

Safe reinforcement learning for multi-energy management systems with known constraint functions

Reinforcement learning (RL) is a promising optimal control technique for multi-energy management systems. It does not require a model a priori - reducing the upfront and ongoing project-specific engineering effort and is capable of learning better representations of the underlying system dynamics. However, vanilla RL does not provide constraint satisfaction guarantees - resulting in various unsafe interactions within its safety-critical environment. In this paper, we present two novel safe RL methods, namely SafeFallback and GiveSafe, where the safety constraint formulation is decoupled from the RL formulation and which provides hard-constraint satisfaction guarantees both during training (exploration) and exploitation of the (close-to) optimal policy. In a simulated multi-energy systems case study we have shown that both methods start with a significantly higher utility (i.e. useful policy) compared to a vanilla RL benchmark (94,6% and 82,8% compared to 35,5%) and that the proposed SafeFallback method even can outperform the vanilla RL benchmark (102,9% to 100%). We conclude that both methods are viably safety constraint handling techniques capable beyond RL, as demonstrated with random agents while still providing hard-constraint guarantees. Finally, we propose fundamental future work to i.a. improve the constraint functions itself as more data becomes available

Evolutionary scheduling of university activities based on consumption forecasts to minimise electricity costs

This paper presents a solution to a predict then optimise problem which goal is to reduce the electricity cost of a university campus. The proposed methodology combines a multi-dimensional time series forecast and a novel approach to large-scale optimization. Gradient-boosting method is applied to forecast both generation and consumption time-series of the Monash university campus for the month of November 2020. For the consumption forecasts we employ log transformation to model trend and stabilize variance. Additional seasonality and trend features are added to the model inputs when applicable. The forecasts obtained are used as the base load for the schedule optimisation of university activities and battery usage. The goal of the optimisation is to minimize the electricity cost consisting of the price of electricity and the peak electricity tariff both altered by the load from class activities and battery use as well as the penalty of not scheduling some optional activities. The schedule of the class activities is obtained through evolutionary optimisation using the covariance matrix adaptation evolution strategy and the genetic algorithm. This schedule is then improved through local search by testing possible times for each activity one-by-one. The battery schedule is formulated as a mixed-integer programming problem and solved by the Gurobi solver. This method obtains the second lowest cost when evaluated against 6 other methods presented at an IEEE competition that all used mixed-integer programming and the Gurobi solver to schedule both the activities and the battery use