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• A physics-informed data-driven model is presented for fuel consumption and battery degradation. • The commuting-oriented hybrid powertrain optimization with multiple objectives is carried out. • The commuting scenario is synthesized by a method combining Markov chain and neural network. • A novel hybrid powertrain with multiple drive modes is modeled based on vehicle bench test data. • The optimized solution can improve the vehicle performance under the commuting driving cycle. The physics-informed data-driven approach is instructive in powertrain modeling with high-fidelity, which can be adopted for powertrain optimization to further improve vehicle performances. In this paper, a plug-in hybrid electric vehicle (PHEV) equipped with a novel powertrain is considered as the research object, and a series of vehicle bench experiments are carried out for operative data acquisition. With the collected data, the physics-informed data-driven models for engine fuel consumption and battery state of health (SOH) loss are proposed. The accuracy and superiority of the proposed model are demonstrated compared with other approaches. In order to realize the commuting-oriented powertrain optimization, the driving cycle representing the commuting scenario is reconstructed based on the collected traffic data in Beijing. The synthesized driving cycle generated by the proposed method can describe the practical application scenario in Beijing within the allowable error range. In addition, the non-dominated sorting genetic algorithm-II (NSGA-II) is adopted to further optimize the parameters for the hybrid powertrain according to vehicle performances. The multi-objective optimization is completed with the engine fuel consumption and battery state of health (SOH) loss as targets. The results indicate that the optimized configuration parameters can reduce fuel consumption by 4.85% and battery capacity loss by 3.7% compared with the original vehicle. [ABSTRACT FROM AUTHOR]

• Synergistic between renewable energy and cogeneration is an ideal transition path. • Imported and exported power could reduce the CO₂ emissions of the heating system. • Investment costs mainly affect the economy of the future heating systems. • Taken Beijing as a case study, the proposed method can be applied to other regions. In response to climate change, China has set the target to achieve carbon neutrality by 2060. The large-scale development of renewable energy and the decarbonization transition of the heating system are crucial for achieving this target. Is there a synergistic effect between the heating system's decarbonization transition and the use of large-scale renewable energy sources? What effect does this synergistic development have on the CO₂ emissions and the economic costs of an energy system? This paper conducts a case study of Beijing, China, and designs three technical path scenarios for heating system decarbonization transition in the future, namely, business as usual scenario where existing policies and regulations are applied, the electricity substitution scenario where the traditional fossil fuel heating system is replaced by electric heat pump, and the synergistic development scenario where the existing thermal power units are retrofitted to replace the consumption of fossil fuels with renewable energy sources. Using the EnergyPLAN model, the hourly operation of Beijing's power and heating systems under the three scenarios are simulated. The research results are: Firstly, the proportion of local renewable energy will be over 70% in all three scenarios, and in the synergistic development scenario, the electricity and heating demand can be met by 100% renewable energy, reducing CO₂ emissions resulted from fossil fuel combustion; secondly, in view of the real-time match of power supply and demand in the power system, at times renewable energy supply would exceed demand. The rational use of the surplus renewable energy can promote further the synergistic development of decarbonization transition of heating system and renewable energy, reducing the primary energy consumption and CO₂ emissions by over 16% and 28% respectively in the future heating system; Thirdly to improve the economic benefit of a synergistic electric power and heating system, focus should be on the reduction of infrastructure construction cost rather than the reduction of fuel consumption. The proposed method can be extended to regional energy systems with more energy sectors, and can be used to formulate decarbonization transition schemes for industry, agriculture, etc. [ABSTRACT FROM AUTHOR]

• A model is proposed for location decision of photovoltaic charging station. • The factors for location analysis and evaluation are determined. • The fuzzy theory is used to improve collection and description of information. • An empirical study is carried out to verify the practicability of the model. • Evaluation criteria with different recession coefficients would change the results. The integration of photovoltaic (PV) power generation system and electric vehicle (EV) charging station can effectively promote the local consumption of renewable energy and reduce the indirect carbon emissions of EV. This paper aims to provide a practical model for location decision of PV charging station (PVCS) which combines geographic information system (GIS) with multi-criteria decision making (MCDM) methods. To verify the feasibility and practicality, an empirical study was conducted in Beijing. First, seven suitable areas were selected preliminarily by suitability analysis of GIS. This stage focused on traffic flow and road distribution. Second, MCDM methods were used to further evaluation. At this stage, a comprehensive evaluation index system of natural, economic, technical and social criteria was established. Interval number, triangular fuzzy number and hesitant fuzzy linguistic term set were utilized to collect and describe evaluation information. Then, the subjective and objective weights of the criteria were calculated by best-worst method and mixed information entropy method, respectively. Finally, the TODIM (an acronym in Portuguese of interactive and MCDM) method was used to rank the alternatives. The ranking results illustrated that alternative sites A3 and A7 are outstanding. Moreover, dual sensitivity analysis and comparative analysis proved the stability and reliability of the model. Scenario analysis expanded the application scope of TODIM method, which also showed that it is necessary to express decision preferences by setting different recession coefficients. This study can provide support for the layout of PVCS in urban, and enrich the application fields of GIS and MCDM methods. [ABSTRACT FROM AUTHOR]