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In this research, twenty-four high capacity (1360 mAh) NMC622/Si-alloy Li-ion full pouch cells with high silicon-alloy content (55%) are cycle aged under seven different cycling conditions to study the effect of different stressors on the cycle life of Si-anode full cells, among which are the effect of ambient temperature, Depth of Discharge (DoD) and the discharge current. The cells are volumetrically constrained at an optimal initial pressure to improve their cycle life, energy and power capabilities. Furthermore, the innovative test setup allows measuring the developed pressure as a result of repeated (de-)lithiation during battery cycling. This uniquely vast testing campaign on Si-anode full cells allows us to study and quantify independently the influence of different stress factors on their cycle life for the first time, as well as to develop a new capacity fade model based on an observed linear relationship between capacity retention and total discharge capacity throughput.

In this research, twenty-four high capacity (1360 mAh) NMC622/Si-alloy Li-ion full pouch cells with high silicon-alloy content (55%) are cycle aged under seven different cycling conditions to study the effect of different stressors on the cycle life of Si-anode full cells, among which are the effect of ambient temperature, Depth of Discharge (DoD) and the discharge current. The cells are volumetrically constrained at an optimal initial pressure to improve their cycle life, energy and power capabilities. Furthermore, the innovative test setup allows measuring the developed pressure as a result of repeated (de-)lithiation during battery cycling. This uniquely vast testing campaign on Si-anode full cells allows us to study and quantify independently the influence of different stress factors on their cycle life for the first time, as well as to develop a new capacity fade model based on an observed linear relationship between capacity retention and total discharge capacity throughput.

When a permanent magnet synchronous motor (PMSM) is designed according to the traditional motor design theory, the performance of the motor is often challenging to achieve the desired goal, and further optimization of the motor design parameters is usually required. However, the motor is a strongly coupled, non-linear, multivariate complex system, and it is a challenge to optimize the motor by traditional optimization methods. It needs to rely on reliable surrogate models and optimization algorithms to improve the performance of the PMSM, which is one of the problematic aspects of motor optimization. Therefore, this paper proposes a strategy based on a combination of a high-precision combined surrogate model and the optimization method to optimize the stator and rotor structures of interior PMSM (IPMSM). First, the variables were classified into two layers with high and low sensitivity based on the comprehensive parameter sensitivity analysis. Then, Latin hypercube sampling (LHS) is used to obtain sample points for highly sensitive variables, and various methods are employed to construct surrogate models for variables. Each optimization target is based on the acquired sample points, from which the most accurate combined surrogate model is selected and combined with non-dominated ranking genetic algorithm-II (NSGA-II) to find the best. After optimizing the high-sensitivity variables, a new finite element model (FEM) is built, and the Taguchi method is used to optimize the low-sensitivity variables. Finally, finite element analysis (FEA) was adopted to compare the performance of the initial model and the optimized ones of the IPMSM. The results showed that the performance of the optimized motor is improved to prove the effectiveness and reliability of the proposed method.

When a permanent magnet synchronous motor (PMSM) is designed according to the traditional motor design theory, the performance of the motor is often challenging to achieve the desired goal, and further optimization of the motor design parameters is usually required. However, the motor is a strongly coupled, non-linear, multivariate complex system, and it is a challenge to optimize the motor by traditional optimization methods. It needs to rely on reliable surrogate models and optimization algorithms to improve the performance of the PMSM, which is one of the problematic aspects of motor optimization. Therefore, this paper proposes a strategy based on a combination of a high-precision combined surrogate model and the optimization method to optimize the stator and rotor structures of interior PMSM (IPMSM). First, the variables were classified into two layers with high and low sensitivity based on the comprehensive parameter sensitivity analysis. Then, Latin hypercube sampling (LHS) is used to obtain sample points for highly sensitive variables, and various methods are employed to construct surrogate models for variables. Each optimization target is based on the acquired sample points, from which the most accurate combined surrogate model is selected and combined with non-dominated ranking genetic algorithm-II (NSGA-II) to find the best. After optimizing the high-sensitivity variables, a new finite element model (FEM) is built, and the Taguchi method is used to optimize the low-sensitivity variables. Finally, finite element analysis (FEA) was adopted to compare the performance of the initial model and the optimized ones of the IPMSM. The results showed that the performance of the optimized motor is improved to prove the effectiveness and reliability of the proposed method.

The decarbonization of industrial processes requires efficient and scalable renewable energy solutions. Concentrated Solar Power (CSP) technology stands out by providing both electricity and high-temperature heat, yet its optimal deployment remains a challenge. This study presents an innovative framework for selecting and optimizing CSP technologies tailored for potential industrial practical applications. In this study, a multi-phase approach is deployed integrating a decision matrix, performance simulations using SOLARPILOT and SAM, and techno-economic evaluation to identify the best CSP solution. The study addresses the feasibility of four candidate CSP technologies, the characteristics of deployment areas, operation parameters such as energy storage time, and characteristics of energy storage material (comparing commercially available materials and an innovative molten salt named FERT-1). The results highlight solar towers as the most suitable technology, while the characteristics of the deployment can lead to over 3.2% difference in annual energy generation (when comparing between two areas, A1 and A2). Regarding energy storage, an optimal storage time of 11 h was identified, achieving a Levelized Cost of Electricity (LCOE) of 24–25 cents/kWh and a 31–32% energy capacity factor. Moreover, regarding energy storage material, the innovative molten salt highlighted improved thermal efficiency.

The decarbonization of industrial processes requires efficient and scalable renewable energy solutions. Concentrated Solar Power (CSP) technology stands out by providing both electricity and high-temperature heat, yet its optimal deployment remains a challenge. This study presents an innovative framework for selecting and optimizing CSP technologies tailored for potential industrial practical applications. In this study, a multi-phase approach is deployed integrating a decision matrix, performance simulations using SOLARPILOT and SAM, and techno-economic evaluation to identify the best CSP solution. The study addresses the feasibility of four candidate CSP technologies, the characteristics of deployment areas, operation parameters such as energy storage time, and characteristics of energy storage material (comparing commercially available materials and an innovative molten salt named FERT-1). The results highlight solar towers as the most suitable technology, while the characteristics of the deployment can lead to over 3.2% difference in annual energy generation (when comparing between two areas, A1 and A2). Regarding energy storage, an optimal storage time of 11 h was identified, achieving a Levelized Cost of Electricity (LCOE) of 24–25 cents/kWh and a 31–32% energy capacity factor. Moreover, regarding energy storage material, the innovative molten salt highlighted improved thermal efficiency.

Biomass from various types of organic waste was tested for possible use in hydrogen production. The composition consisted of lignified samples, green waste, and kitchen scraps such as fruit and vegetable peels and leftover food. For this purpose, the enzymatic pretreatment of organic waste with a combination of five different hydrolytic enzymes (cellulase, amylase, glucoamylase, pectinase and xylase) was investigated to determine its ability to produce hydrogen (H2) with the hydrolyzate produced here. In course, the anaerobic rod-shaped bacterium T. neapolitana was used for H2 production. First, the enzymes were investigated using different substrates in preliminary experiments. Subsequently, hydrolyses were carried out using different types of organic waste. In the hydrolysis carried out here for 48 h, an increase in glucose concentration of 481% was measured for waste loads containing starch, corresponding to a glucose concentration at the end of hydrolysis of 7.5 g·L−1. In the subsequent set fermentation in serum bottles, a H2 yield of 1.26 mmol H2 was obtained in the overhead space when Terrific Broth Medium with glucose and yeast extract (TBGY medium) was used. When hydrolyzed organic waste was used, even a H2 yield of 1.37 mmol could be achieved in the overhead space. In addition, a dedicated reactor system for the anaerobic fermentation of T. neapolitana to produce H2 was developed. The bioreactor developed here can ferment anaerobically with a very low loss of produced gas. Here, after 24 h, a hydrogen concentration of 83% could be measured in the overhead space.