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Vehicle speed prediction plays a critical role in energy management strategy (EMS). Based on the adaptive particle swarm optimization–least squares support vector machine (APSO-LSSVM) algorithm with BP neural network (BPNN), a novel closed-loop vehicle speed prediction system is proposed. The database of a vehicle internet platform was adopted to construct a speed prediction model based on the APSO-LSSVM algorithm. Furthermore, a BPNN is established according to the local high-precision nonlinear fitting relationship between the predicted value and error so as to correct the prediction value. Then, the results are returned to the APSO-LSSVM model for calculating the minimum fitness function, thus obtaining a closed-loop prediction system. Finally, equivalent fuel consumption minimization strategy (ECMS) based EMS was performed. According to the simulation results, the RMSE performance is 0.831 km/h within 5 s, which is over 20% higher than other performances. Additionally, the training time is 15 min within 5 s, which is advantageous over BPNN. Furthermore, fuel consumption increases by 6.95% compared with the dynamic-programming algorithm and decreased by 5.6%~10.9% compared with the low accuracy of speed prediction. Overall, the proposed method is crucial for optimizing EMS as it is not only effective in improving prediction accuracy but also capable of reducing training time.

In order to mitigate the power density shortage of current energy storage systems (ESSs) in pure electric vehicles (PEVs or EVs), a hybrid ESS (HESS), which consists of a battery and a supercapacitor, is considered in this research. Due to the use of the two ESSs, an energy management should be carried out for the HESS. An optimal energy management strategy is proposed based on the Pontryagin's minimum principle in this research, which instantaneously distributes the required propulsion power to the two ESSs during the vehicle's propulsion and also instantaneously allocates the regenerative braking energy to the two ESSs during the vehicle's braking. The objective of the proposed energy management strategy is to minimize the electricity usage of the EV and meanwhile to maximize the battery lifetime. A simulation study is conducted for the proposed energy management strategy and also for a rule-based energy management strategy. The simulation results show that the proposed strategy saves electricity compared to the rule-based strategy and the single ESS case for the three typical driving cycles studied in this research. Meantime, the proposed strategy has the effect of prolonging the battery lifetime compared to the other two cases.

Abstract Heat pipes have been extensively applied in the domain of energy conversion and heat recovery application. A novel radial heat pipe with internally finned condenser has been proposed to recover waste heat from air conditioning units. Full mathematical description and computational fluid dynamics model were developed to predict the loop flow and thermal performance in a single radial heat pipe, respectively. The research focuses mainly on the effects of input power, filling ratio, pipe diameter, and velocity of the cooling water on the thermal performance and entropy generation rate of this novel heat pipe. Numerical results illustrate that average reductions in total thermal resistance with utilization of fins approximately achieved 8.69% and 14.78% for fins nf = 4 and nf = 8, respectively. Similarly, entropy generation rate shows an average decrease of 19.4% and 37.5% for identical operations. Additionally, the effect of internally finned condenser on the multiphase fluid flow has been comprehensively analyzed, including the break of the condensed film, generation of bubbles and nucleation boiling. The results further indicate that incorporation of internal fins has a significant influence on enhancing thermal homogenization and waste thermal recovery efficiency of a radial heat pipe. Case results agreed well with experimental data within average deviation being no more than 10%.

Abstract The upward energy demand, along with the depletion of conventional energy sources, demands improved utilization of renewable energy resources. Among all renewable energy resources, solar energy is the most appropriate alternative to conventional energy sources owing to its inexhaustibility and green property. Solar collectors are devices that convert solar radiation into heat or energy. However, the efficiency of the solar collector is still not adequate. The competent step to enhance the efficiency of the solar collector is to use nanofluids. This study is carried out different phases viz. characterization and stabilization while both qualitative and quantitative methods used to evaluate the stability of nanofluids thermophysical properties of Al2O3 and CNC nanofluids such as thermal conductivity measured at four different temperature using KD2 Pro, viscosity and specific heat determined at similar temperature range by viscometer and differential scanning calorimetry respectively. The experiment is executed with a fixed flow rate and in steady-state conditions under extensive solar radiation. The experimental study has revealed that up to 2.48% and 8.46% efficiency of solar collector enhanced by using 0.5% Al2O3 and 0.5% CNC nanofluids respectively. Moreover, nanofluids show good to moderate stability performance. Besides, the thermal conductivity of nanofluids increased while viscosity is in a decreasing trend with increasing temperature. Nanofluids could enhance the efficiency of a flat-plate solar collector.

Abstract Phase-change materials (PCMs) can store and release great amount of thermal energy during the phase change and thus have broad application prospects in thermal energy management, waste heat recovery, building energy conservation, and other fields. However, the before and after state changes of PCMs are often accompanied by liquid leakage, thus severely limiting their application. Introducing supporting materials can solve this leakage problem but at the expense of phase-change enthalpy and service life. Herein, a novel comb-like structural phase-change composite with high latent heat was designed by using poly (ethylene glycol) (PEG) chain, which tightly intertwines with a comb-like structural phase-change supporting material under induced dipole force due to structural compatibility. This material can achieve shape stability and obtain high phase-change enthalpies (168.9 J/g–200.3 J/g). Furthermore, the composite does not show phase separation due to the good compatibility, and its phase-change temperatures and enthalpies can be adjusted by regulating the content and molecular weight of the loaded PEG.

Abstract Specification by central government of the heating levels which are to be maintained in British school buildings has recently been altered. This paper is concerned with examining the nature of changes that have been made by comparing present requirements with their counterparts during the preceding one hundred years. Attention is focused on the apparently contradictory implications of these changes for those charged with responsibility for maintaining heating levels in school buildings while, at the same time, conserving fuel. It is suggested that the new statutory requirements present those who are responsible with a duty which may, in practical terms, prove difficult or costly to discharge. Although discussion is specifically restricted to British school buildings, issues are raised which are pertinent to attempts to integrate regulation of heating with control of fuel consumption in other types of non-domestic buildings both in Britain and abroad.

In order to enhance the efficiency and benefits of the sludge anaerobic digestion process, K2FeO4 was added to a sludge anaerobic digestion system, and its effects on the system were comprehensively investigated. Results showed that sludge anaerobic digestion was greatly improved by adding 500 mg/L K2FeO4. Biogas and methane productions were increased by 26.6 and 28.4%, respectively. Sludge reduction, protein removal, and the conversion efficiency of dissolved organics were all improved. The mechanism revealed that the disintegration of sludge flocs, enhancement of protease activity, and decrease of soluble sulfide toxicity on microorganisms contributed to biogas production and sludge reduction. Biogas quality was improved, benefitting from the decreasing H2S content in biogas; as additionally, the cost of biogas desulfuration was reduced. In the biogas slurry treatment, the PO43--P concentrations were decreased by 39%, which also reduced the cost of the dephosphorization processes at certain extent.

AbstractSmart Grids are electrical grids that require a decentralised way of controlling electric power conditioning and thereby control the production and distribution of energy. Yet, the integration of Distributed Renewable Energy Sources (DRESs) in the Smart Grid introduces new challenges with regards to electrical grid balancing and storing of electrical energy, as well as additional monetary costs. Furthermore, the future smart grid also has to take over the provision of Ancillary Services (ASs). In this paper, a distributed ICT infrastructure to solve such challenges, specifically related to ASs in future Smart Grids, is described. The proposed infrastructure is developed on the basis of the Smart Grid Architecture Model (SGAM) framework, which is defined by the European Commission in Smart Grid Mandate M/490. A testbed that provides a flexible, secure, and low-cost version of this architecture, illustrating the separation of systems and responsibilities, and supporting both emulated DRESs and real hardware has been developed. The resulting system supports the integration of a variety of DRESs with a secure two-way communication channel between the monitoring and controlling components. It assists in the analysis of various inter-operabilities and in the verification of eventual system designs. To validate the system design, the mapping of the proposed architecture to the testbed is presented. Further work will help improve the architecture in two directions; first, by investigating specific-purpose use cases, instantiated using this more generic framework; and second, by investigating the effects a realistic number and variety of connected devices within different grid configurations has on the testbed infrastructure.