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To study the heating performance of a solar-assisted cold-water phase-change-energy heat pump system, its heating performance under series and parallel connections is simulated for a community in Harbin, the influence of ice thickness on the different operation modes is analyzed, and the economy of the system is calculated for series and parallel connections in this paper. The results show that the water supply temperature is higher and more uniform in the parallel operation, and more terminal heat is supplied; the ice thickness has more of an influence on the series connection compared to the parallel connection; and the dynamic payback period is 6.72 years for the series connection and 7.28 years for the parallel connection. This case study can serve as a guide for practical engineering application projects and act as a reference for heating and economic data for the promotion of this heat pump system.

To study the heating performance of a solar-assisted cold-water phase-change-energy heat pump system, its heating performance under series and parallel connections is simulated for a community in Harbin, the influence of ice thickness on the different operation modes is analyzed, and the economy of the system is calculated for series and parallel connections in this paper. The results show that the water supply temperature is higher and more uniform in the parallel operation, and more terminal heat is supplied; the ice thickness has more of an influence on the series connection compared to the parallel connection; and the dynamic payback period is 6.72 years for the series connection and 7.28 years for the parallel connection. This case study can serve as a guide for practical engineering application projects and act as a reference for heating and economic data for the promotion of this heat pump system.

Accurate estimation of State-of-Charge (SoC) is essential for ensuring the safe and efficient operation of electric vehicles (EVs). Currently, second-order RC equivalent circuit models do not account for the influence of battery charging and discharging states on battery parameters. Additionally, offline parameter identification becomes inaccurate as the battery ages. Online identification requires real-time parameter updates during the SoC estimation process, which increases the computational complexity and reduces the computational efficiency of real vehicle Battery Management System (BMS) chips. To address these issues, this paper proposes a SoC estimation method that combines online and offline identification based on an optimized second-order RC equivalent circuit model, which distinguishes it from existing methods in the field. On the basis of the traditional second-order RC model, the Ohmic resistance (R0), polarization resistance (R1), polarization capacitance (C1), diffusion resistance (R2), and diffusion capacitance (C2) during the charging and discharging processes are discussed separately. R0, which does not change frequently, is identified offline, while R1, R2, C1, and C2, which dynamically change with time and current, are identified online. To thoroughly verify the feasibility of the proposed method, we construct an SoC estimation test bench, which allows us to adjust the battery’s surface temperature in real time using a temperature control chamber. Experimental validation under Federal Urban Driving Schedule (FUDS) (−10 °C to 45 °C, 80% battery capacity) and Dynamic Stress Test (DST) (−10 °C to 45 °C, 8% battery capacity) conditions demonstrate that our method improves SoC estimation accuracy by 16.28% under FUDS and 28.2% under DST compared to the improved GRU-based transfer learning method, while maintaining system SoC estimation efficiency.

Accurate estimation of State-of-Charge (SoC) is essential for ensuring the safe and efficient operation of electric vehicles (EVs). Currently, second-order RC equivalent circuit models do not account for the influence of battery charging and discharging states on battery parameters. Additionally, offline parameter identification becomes inaccurate as the battery ages. Online identification requires real-time parameter updates during the SoC estimation process, which increases the computational complexity and reduces the computational efficiency of real vehicle Battery Management System (BMS) chips. To address these issues, this paper proposes a SoC estimation method that combines online and offline identification based on an optimized second-order RC equivalent circuit model, which distinguishes it from existing methods in the field. On the basis of the traditional second-order RC model, the Ohmic resistance (R0), polarization resistance (R1), polarization capacitance (C1), diffusion resistance (R2), and diffusion capacitance (C2) during the charging and discharging processes are discussed separately. R0, which does not change frequently, is identified offline, while R1, R2, C1, and C2, which dynamically change with time and current, are identified online. To thoroughly verify the feasibility of the proposed method, we construct an SoC estimation test bench, which allows us to adjust the battery’s surface temperature in real time using a temperature control chamber. Experimental validation under Federal Urban Driving Schedule (FUDS) (−10 °C to 45 °C, 80% battery capacity) and Dynamic Stress Test (DST) (−10 °C to 45 °C, 8% battery capacity) conditions demonstrate that our method improves SoC estimation accuracy by 16.28% under FUDS and 28.2% under DST compared to the improved GRU-based transfer learning method, while maintaining system SoC estimation efficiency.

Uncertainty commonly exists in the wireless power transfer (WPT) systems for moving objects. To enhance the robustness of the WPT system to uncertain parameter variations, a modified WPT system structure and an interval-based uncertain optimization method are proposed in this paper. The modified WPT system, which includes two Q-type impedance matching networks, can switch between two different operating modes. The interval-based uncertain optimization method is used to improve the robustness of the modified WPT system: First, two interval-based objective functions (mean function and variance function) are defined to evaluate the average performance and the robustness of the system. A double-objective uncertain optimization model for the modified WPT system is built. Second, a bi-level nested optimization algorithm is proposed to find the Pareto optimal solutions of the proposed optimization model. The Pareto fronts are provided to illustrate the tradeoff between the two objectives, and the robust solutions are obtained. Experiments were carried out to verify the theoretical method. The results demonstrated that using the proposed method, the modified WPT system can achieve good robustness when the coupling coefficient, the operating frequency, the load resistance or the load reactance varies over a wide range.

Uncertainty commonly exists in the wireless power transfer (WPT) systems for moving objects. To enhance the robustness of the WPT system to uncertain parameter variations, a modified WPT system structure and an interval-based uncertain optimization method are proposed in this paper. The modified WPT system, which includes two Q-type impedance matching networks, can switch between two different operating modes. The interval-based uncertain optimization method is used to improve the robustness of the modified WPT system: First, two interval-based objective functions (mean function and variance function) are defined to evaluate the average performance and the robustness of the system. A double-objective uncertain optimization model for the modified WPT system is built. Second, a bi-level nested optimization algorithm is proposed to find the Pareto optimal solutions of the proposed optimization model. The Pareto fronts are provided to illustrate the tradeoff between the two objectives, and the robust solutions are obtained. Experiments were carried out to verify the theoretical method. The results demonstrated that using the proposed method, the modified WPT system can achieve good robustness when the coupling coefficient, the operating frequency, the load resistance or the load reactance varies over a wide range.

Biogas production is a clean renewable energy source that can improve lives in developing countries. However, winter temperatures in some areas are too low to enable enough biogas production in small unheated digesters to meet the energy requirements of households. Low-cost, high yield reactors adapted to the local climate are needed in those situations. A decision-support model was developed to assist in the design of biogas reactors capable of meeting households’ year-round energy needs. Monthly biogas production relative to household energy needs was calculated for the scenario of suburban Hanoi, Vietnam. Calculations included pig number, slurry (manure water mixture) dilution, retention time and biogas/solar heating. Although using biogas to heat the digester increased biogas production, it did not lead to an energy surplus, particularly with the 1:9 slurry dilution rate commonly used on pig farms. However, at a 1:3 slurry dilution, the use of solar heating to provide 90% and biogas 10% of the heat required to heat the digester to 35 °C improved the biogas production by 50% compared to psychrophilic production. The energy needs of an average five-person family throughout the year required 17 fattening pigs. This model can establish the best solution for producing sufficient energy throughout the year.