• Energy Research
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Energy management is an enabling technique to guarantee the reliability and economy of hybrid electric systems. This paper proposes a novel machine learning-based energy management strategy for a hybrid electric bus (HEB), with an emphasized consciousness of both thermal safety and degradation of the onboard lithium-ion battery (LIB) system. Firstly, the deep deterministic policy gradient (DDPG) algorithm is combined with an expert-assistance system, for the first time, to enhance the “cold start” performance and optimize the power allocation of HEB. Secondly, in the framework of the proposed algorithm, the penalties to over-temperature and LIB degradation are embedded to improve the management quality in terms of the thermal safety enforcement and overall driving cost reduction. The proposed strategy is tested under different road missions to validate its superiority over state-of-the-art techniques in terms of training efficiency and optimization performance.

In this paper, fault prognosis of aircraft jet engines are considered using computationally intelligent-based methodologies to ensure flight safety and performance. Two different dynamic neural networks namely, the nonlinear autoregressive neural networks with exogenous input (NARX) and the Elman neural networks are developed and designed for this purpose. The proposed dynamic neural networks are designed to capture the dynamics of two main degradations in the jet engine, namely the compressor fouling and the turbine erosion. The health status and condition of the engine is then predicted subject to occurrence of these deteriorations. In both proposed approaches, two scenarios are considered. For each scenario, several neural networks are trained and their performance in predicting multi-flights ahead turbine output temperature are evaluated. Finally, the most suitable neural network for prediction is selected by using the normalized Bayesian information criterion model selection. Simulation results presented demonstrate and illustrate the effective performance of our proposed neural network-based prediction and prognosis strategies.

Abstract Recharging infrastructure (RI) deployment plays a vital role in improving the public recharging availability for transport electrification. Decarbonizing transportation using low-emission electricity requires massive RI network. Even though the consumers are reluctant to purchase electric vehicles (EVs) until RIs are sufficiently placed, the investors are not willing to invest in RIs due to recharging demand uncertainties. Therefore, a scientific planning framework is needed to ensure the sustainable deployment of EV-RIs in complex networks. In this study, a lifecycle thinking-based multi-period infrastructure-planning framework is proposed to develop sustainable public EV-RIs in an urban context. This framework consists of a temporal model to find the dynamic EV-RI demands, a stochastic model to obtain travel distances, and a multi-objective optimization model to select the best desirable capacities and locations for potential EV-RIs. A case study of a typical medium-scale municipality in Canada was assessed using the proposed framework and validated using conventional infrastructure planning scenarios. The geo-processing data, regional travel behaviors, and recharging characteristics were used as model inputs. The results of the case study showed that the proposed framework can be used to estimate multi-period public recharging demands, minimize lifecycle costs, maximize service coverage and infrastructure utilization, and ensure reasonable paybacks compared to conventional planning approaches. Moreover, this framework can be used to compare different investment assistances, which are required in the early stages of the RI deployment process to encourage investors. Furthermore, government and private institutions can use this framework to identify recharging demands, permitting, and developing RIs in the long-run.

Abstract The residential sector accounts for most of energy-consumption in developing countries in the form of traditional energy. The use of commercial energy is nominal and confined mostly to urban areas where fuelwood is already monetized. A model, based on an end-use/process analysis approach, is developed on a spreadsheet, which is capable of simulating scenarios to address issues of increasing traditional energy-demand caused by population growth, sustainable supply capacity of the existing energy resources, potential for development of new and renewable energy resources, technology. This paper is divided into two parts: general energy issues and the modelling approach, and the application of this approach to Nepal in the context of fuelwood-supply sustainability.

Additional file 1. Histotical and projected C stocks and fluxes.

These data sets provide the WRF [1] calculated wind data for Pritzwalk (onshore) and FINO3 (offshore) as Python dictionaries. Additionally, the files contain k-means cluster objects derived from these profiles. These data sets were used for power assessment and design exploration of Airborne Wind Energy Systems using the awebox [2] optimization toolbox. WRF setups are described in detail and used in publication [3,4,5]. Wind data are interpolated to fixed heights of: [10, 28, 50, 70, 90, 100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600, 700, 800, 1000, 1200] meters above ground. Onshore wind data: Location lat: 53° 10.78' N; long: 12° 11.35' E Time: 1 September 2015 - 31 August 2016 Timestep: 10 min Offshore wind data: Location lat: 55° 11.7' N, long: 7° 9.5' E Time: 1 September 2013 - 31 August 2014 Timestep: 10 min The clusters are derived from both horizontal wind velocity components using the scikit-learn’s k-means clustering algorithm [6]. For our purposes, wind vectors were rotated such that the main wind speed always points in the same direction (u_main,u_deviation). [1]: Weather Research and Forecasting Model [2]: awebox [3]: Improving mesoscale wind speed forecasts using lidar-based observation nudging for airborne wind energy systems [4]: Offshore and onshore ground-generation airborne wind energy power curve characterization [5]:Ground-generation airborne wind energy design space exploration [6]: sklearn.cluster.KMeans

Select monthly climate data for provinces in Canada. Monthly data includes mean temperature, maximum temperatures, minimum temperature, snow, precipitation, HDD, CDD, and Trade.

Rapid, ongoing permafrost thaw of peatlands in the discontinuous permafrost zone is exposing a globally significant store of soil carbon (C) to microbial processes. Mineralisation and release of this peat C to the atmosphere as greenhouse gases is a potentially important feedback to climate change. Here we investigated the effects of permafrost thaw on peat C at a peatland complex in western Canada. We collected 15 complete peat cores (between 2.7 abd 4.5 m deep) along four chronosequences, from elevated permafrost plateaus to saturated thermokarst bogs that thawed up to 600 years ago. The peat cores were analysed for peat C storage and peat quality, as indicated by decomposition proxies (FTIR and C/N ratios) and potential decomposability using a 200-day aerobic incubation. Our results suggest net C loss following thaw, with average total peat C stocks decreasing by ~19.3 +/- 7.2 kg C m-2 over <600 years (~13% loss). Average post-thaw accumulation of new peat at the surface over the same period was ~13.1 +/- 2.5 kg C m-2. We estimate ~19% (+/- 5.8%) of deep peat (>40 cm below surface) C is lost following thaw (average 26 +/- 7.9 kg C m-2 over <600 years). Our FTIR analysis shows peat below the thaw transition in thermokarst bogs is slightly more decomposed than peat of a similar type and age in permafrost plateaus, but we found no significant changes to the quality or lability of deeper peat across the chronosequences. Our incubation results also showed no increase in C mineralisation of deep peat across the chronosequences. While these limited changes in peat quality in deeper peat following permafrost thaw highlight uncertainty in the exact mechanisms and processes for C loss, our analysis of peat C stocks shows large C losses following permafrost thaw in peatlands in western Canada.

This repository contains the raw data of the inputs and results presented in the paper "Electrification of light-duty vehicle fleet alone will not meet mitigation targets" published in Nature Climate Change (2020) by Alexandre Milovanoff, I. Daniel Posen, and Heather L. MacLean (Department of Civil & Mineral Engineering, University of Toronto).