Persistently exploring ever more sustainable and economically promising alternative protein sources is a grand challenge that humanity must tackle today. Edible insects, as one long-standing food source for mankind, are attracting considerable attention in both academia and capital markets due to the exacerbated global food crisis and new technological advancements. To massively and safely rear edible insects, all stages in their lifecycle must be carefully engineered, monitored, and controlled. This work aims to first investigate the natural mating and ovipositing behaviors of black soldier flies and then engineer an artificial environment that well suits their needs. In particular, a modularized and scalable facility equipped with dedicated light recipe and climate control has been designed and built. The results of our comparative experiments revealed that black soldier flies in the artificial facility tended to have better mating and oviposition quantitatively and qualitatively as compared to the control group in the nature environment. As a result, this work demonstrated that within a well-controlled and comfortable artificial environment, it is possible and feasible to stably breed black soldier flies in large scale. This paves the foundation for industrialized insect-based bioconversion.

Due to the depletion of fossil fuels and the rise of electric vehicles, electric buses have emerged as a new means of transportation, helping to reduce air pollution and energy consumption. However, the limited battery capacity of electric buses makes on-the-go charging a major concern, as installing chargers at every bus stop is impractical due to costs. Therefore, employing appropriate charging strategies for electric buses is crucial. A critical issue that urgently needs resolution is determining the optimal timing and quantity for charging electric buses, considering the current size of the bus fleet, routes, and charging station facilities. Battery aging, which affects battery capacity and thereby influences charging decisions and the operating costs of bus routes, must be considered. This paper proposes a hybrid integer programming model to describe bus line operations and uses a semi-empirical method to estimate battery aging. The Gurobi solver is used to select an appropriate solution strategy to solve the dualobjective mathematical model in this paper, so as to reduce the operating cost of bus lines and the aging of batteries. The results show that the model proposed in this paper can effectively reduce the operating cost of the bus route and the aging of the battery.

Battery failure represents one of the most common threats to electric vehicles (EVs). Existing diagnosis methods for onboard Lithium-ion batteries are heavily limited by complex real-world scenarios and struggle to handle early faults. With the aim of detecting battery faults in an early stage, this article proposes a deep learning-enabled fault diagnosis framework that blends the advantages of Gramian angular difference fields (GADF) and Transformer-based networks. First, a median-difference-process is developed to capture the dynamic electrical behaviors of cells. Then, the voltages of cells are converted into GADF matrices and presented as grayscale images. Afterward, a Vision Transformer is introduced to extract and learn the features of different battery fault patterns. Experimental verification in a real-world EV battery pack indicates that the proposed method achieves fault diagnosis for early battery failures with an accuracy of 97.30$\%$. Moreover, it outperforms existing methods and maintains a high recall rate of 94.77$\%$. Consequently, the proposed strategy proves to be effective for real-world applications.

State-of-charge (SOC) is a critical parameter of battery management systems to ensure safe, efficient, reliable and durable battery operations. However, SOC cannot be directly measured and is highly sensitive to different temperatures. Thus, the SOC estimation accuracy and uncertainty management are significant for robust control and energy dispatch. Gaussian process regression (GPR) is thus becoming appealing due to its non-parametric and interpretable probabilistic advantages. But the scalability of GPR is still challenging, suffering from cubic complexity. To solve these challenges, a SOC estimation method is proposed by an end-to-end scalable deep recurrent structure with fast transfer learning at different temperatures. First, convolutional and recurrent neural networks are employed to catch nonlinear temporal dependency within measurements. Second, a GPR layer is concatenated after neural networks, so that the estimation can be quantified with uncertainty while retaining nonlinear expressive ability. Then, a non-parametric fast transfer learning is designed to realize fast transfer between different temperatures. Next, structured sparse approximations and a semi-stochastic gradient procedure are established for scalable training. Finally, the accuracy and fast transfer of the proposed structure are verified through comparison. The structure demonstrates the state-of-the-art performance on estimation accuracy and efficiency with transfer learning faster than fine-tuning strategy by two orders of magnitude.

Lithium-ion batteries are extensively utilized in applications ranging from portable electronics to electric vehicles and renewable energy systems. Accurate prediction of the state of power capacity (SOP) in lithium-ion batteries is fundamental for guaranteeing the safe, reliable, and efficient operation of these systems. However, most existing SOP prediction algorithms only account for the external measurable state constraints of the battery, ignoring the influence of the internal electrochemical states. Using electrochemical models to model batteries can introduce the electrochemical perspective, but many related methods ignore the impact of temperature variations on model parameters. Therefore, this paper proposes an SOP estimation framework based on a physics-informed data-driven approach, which fully integrates the electrochemical model and battery operation data to provide accurate power capacity estimation against temperature effects. First, the battery is modeled using an electrochemical model, and the battery operation data is used to identify the electrochemical temperature-sensitive parameters to enhance the accuracy of the model. Secondly, safety constraints for battery operations are introduced from the perspective of the battery mechanisms and the bisection method is employed to search for the maximum current. Compared with the SOP calibration results and the state-of-the-art method, the results highlight the accuracy of the proposed method. Finally, by referring to the characteristic maps-based method and employing Gaussian process regression, the search interval of SOP is significantly reduced based on historical data, reducing the search time by 80%.

Developing a fast and safe charging strategy has been one of the key breakthrough points in lithium battery development owing to its range anxiety and long charging time. The majority of current model-based charging strategies are developed for deterministic systems. Real battery dynamics are, however, affected by model mismatches and process uncertainties, which may lead to constraint violations and even premature aging. This article proposes a fast charging scheme based on distributionally robust model predictive control (DRMPC) against uncertainty. Specifically, a coupled electrothermal-aging model is first introduced to describe the battery behavior, and electrothermal parameters of the adopted model are identified online based on the recursive least-squares algorithm. Subsequently, an online DRMPC-based charging framework is proposed, utilizing the Wasserstein ball centered on the empirical distribution to characterize uncertainty. Finally, the proposed algorithm is compared to model predictive control and constant current-constant voltage algorithms, and its effectiveness is validated on a real battery simulator. Results show that the proposed algorithm can handle the uncertainty effectively while satisfying the constraints, and significantly improve the charging speed.

The demand for permanent magnet resolvers is rising significantly with the increase of operation speed in the electric propulsion system. In this paper, the rotor-pole number (RPN) cooperation strategy is proposed from the perspective of phase shift on the modular flux switching permanent magnet (FSPM) resolver with yokeless stator. The RPN cooperation strategy is deduced analytically by adjusting the angle fitting ratio under the fixed 90-degree sine and cosine winding phase shift electric angle. Moreover, its restrictions are also derived by the flux leakage and permeance analysis in certain cases. Therefore, the proposed cooperation strategy and its limitations enrich the field modulation principle in the PM resolver application. Based on the proposed cooperation strategy and its limitation, a series of proper rotor and stator combinations are proposed in the FSPM resolver with no more than 1.5% total harmonic distortion (THD) evaluated in finite element analysis (FEA).

The heterogeneity of cells in a battery pack is inevitable but brings high risks of premature failure and even safety hazards. Accordingly, for safe and long-life operation, it is necessary to adjust the state of charge (SOC) of all in-pack cells to the same level. To address this problem, this article first proposes a battery SOC observer and analyzes its stability and convergence analysis using the Lyapunov direct method. Different to most available estimators is that the proposed method does not require the information of cell capacities. Then, after modeling the equalization system as a directed graph, the equalization problem is cast as a path searching problem. Finally, an A-star algorithm subject to balancing constraints is proposed to find the shortest path in this graph, corresponding to the most efficient SOC equalization. Experimental results show that the steady-state error of the proposed observer is less than $2\%$. It also demonstrates that the A-star algorithm can decrease the balancing time and energy loss during the balancing process by 9.59% and 19.5%, respectively, relative to the mean-difference-average method.

Degradation dynamics modeling and health prognosis play extremely important roles in system prognostics and health management. Wiener process-based degradation models and remaining useful life (RUL) prediction methods have the advantage of high flexibility and efficiency, with features such as Brownian motion with drift and scale parameters. They can also quantify prediction uncertainty through inverse Gaussian distribution. However, prior studies use offline-identified model parameters, which can result in difficulties in both model adaptability and health prognosis. To improve the performance of Wiener process models, this article proposes a new data-driven Brownian motion model that utilizes the adaptive extended Kalman filter (AEKF) parameter identification method. The proposed model can update model parameters online and adapt to uncertain degradation operations. This data-driven method has the flexibility and efficiency of Brownian motion models but avoids their shortcomings in model adaptability and health prognosis. The model parameters and drift parameter are online estimated based on AEKF using limited historical system measurements. The effectiveness of the proposed data-driven framework in degradation modeling and RUL prediction is evaluated through simulations and experimental results on lithium-ion battery degradation data. The results show that the proposed approach has significant accuracy and robustness for both model adaptability and RUL prediction.

Microgrid is a key enabling solution to future smart grids by integrating distributed renewable generators and storage systems to efficiently serve the local demand. However, due to the intermittent and uncertainty of distributed renewable energy, the reliability and economic operations of microgrid are facing increasing new challenges. Traditionally, economic dispatch issue is considered as solving an offline or online optimization problem whose objective function is prior known. However, accurate and determined function expression is difficult to formulate, and wrong expression may result in waste of electricity cost and causing security issues. Thus, it is desirable to reformulate the economic dispatch problem, and solve it in a data-driven way. This paper proposes a data-driven energy management solution based on Bayesian optimization algorithm (BOA) for a single grid-connected home microgrid. The proposed solution formulates the optimization problem without a closed-form objective function expression, and solves it using BOA-based data-driven framework. The proposed solution is a kind of black-box function sequential global optimization strategy, and does not require derivative operation on the objective function. Besides, it can also solve the microgrid operation and parameter prediction uncertainty. Simulation results demonstrate the effectiveness of the proposed solution.