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Current battery management systems (BMSs) in automotive applications monitor and control batteries in a relatively simple, conservative manner, with limited capabilities of sensing, estimation, proactive controls, and fault diagnosis. With ever-increasing computing power onboard and/or in the cloud, enhanced environmental perception and vehicular communications, emerging electrified vehicles and smart grids provide unprecedented opportunities for designing and developing next-generation smart BMSs. However, three entrenched technical challenges need to be addressed, including 1) limited knowledge of battery internal states and parameters; 2) poor adaptability to extreme operating conditions; and 3) lack of efficient predictive maintenance, resulting in great concern for battery safety and economy. This paper aims to present some critical insights into possible solutions to the three challenges. First, the multi-physics coupled battery modeling concept is introduced to emphasize that looking at mechanical-electrochemical-thermal-aging dynamics is critically important for devising revolutionary BMS algorithms. Second, electrothermal modeling, advanced optimization routines, and predictive control with vehicular autonomy and connectivity facilitate innovative designs in dynamically hysteresis-aware thermal management, heat transfer under extreme fast charging, and preheating in a cold climate. Third, battery models and machine learning are complementary and can be very useful for improving battery remaining useful life prediction and fault diagnosis, achieving high-efficiency predictive maintenance.

Subcritical hydrothermal liquefaction (HTL) is an efficient approach for bio-oil production from wet sewage sludge (SS). Before HTL, pretreatment of wet SS by sodium hypochlorite (NaClO) or combination of NaClO and ultrasonic treatment was implemented with different NaClO dosage or ultrasonic temperature for the first time. The results demonstrated that TOC and content of protein in EPS of SS increased after two pretreatment methods. Meanwhile, the bio-oil yield was improved and the maximum of 39.4 wt% was obtained from the bio-oil under combined pretreatment (9.39 mg NaClO/g dried SS and ultrasounds at 80 °C) when HTL was performed at 300 °C for 15 min. Additionally, the maximum energy recovery reached 58.0% with the highest carbon recovery (61.9%) and the highest hydrogen recovery (39.0%) in bio-oils obtained from HTL of combined pretreated SS. GC-MS and NMR analysis showed that the chemical compositions of bio-oil were not affected significantly by pretreatment methods but relative percentage of each compound was changed. The esters formation was favored after combined pretreatment and increased to 16.3%, while the contents of acids and N-containing compounds decreased by 8.2% and 4.0%, respectively. TGA of bio-oils revealed that both pretreatments could promote the formation of light oil components such as diesel. Finally, the main possible reaction pathways for HTL of SS have been elucidated. Overall, this research provided a theoretical foundation for utilization of wet SS from sewage treatment plant directly for bioenergy production.

The utilization of repurposed second-life batteries from electric vehicles in DC microgrids presents a sustainable and cost-effective solution. However, efficiently integrating these batteries within DC microgrids poses two main challenges: (1) poor consistency among retired battery packs; (2) fluctuations in the DC bus voltage. Traditional approaches primarily focus on achieving the state of charge (SOC) balancing within retired battery packs, often neglecting the suppression of voltage fluctuations, thereby compromising system stability. To address these challenges, this paper proposes a virtual DC machine (VDCM)-based control strategy that enhances both the power quality of the DC microgrid and the consistency of retired battery packs. On the one hand, reconfigurable topology and SOC balancing strategies are developed to manage the equalization current, achieving SOC balancing within the retired battery packs. On the other hand, a VDCM strategy is designed to leverage the capacity characteristics of retired batteries to increase the inertia of the DC microgrid. Compared to conventional methods, experimental results show that the proposed strategy not only maintains SOC balancing during both the charging and discharging phases but also significantly reduces overshoot and settle times of dc voltage to 64.2% and 55.6%, respectively.

The anomalous measurements pose significant challenges for the secure and economical operation of multiple microgrids (MMGs). However, existing works still cannot effectively address this problem. Therefore, this paper proposes a robust decentralized multi-agent deep reinforcement learning (MADRL) control approach by developing a novel actor network on the basis of the centralized training and decentralized execution framework (CTDEF). To achieve robust control of MMG systems against anomalous data, this approach extracts the variation patterns of measurements from both temporal and spatial perspectives. From the spatial perspective, the measurements are first cast to a graph, and a multi-head graph attention (MGAT) network is employed to extract the structural correlations among these measurements. From the temporal perspective, the measurements feature extracted by MGAT along with the state and historical actions are processed by two recurrent networks to obtain the trajectory history feature of each MG. The confederate image technology is developed therein for each agent to infer the intentions of other agents in order to better extract the trajectory history. To more fully express the structural correlations between nodes and decision intentions of other agents, a node cognition regularizer and a mutual information-based regularization term are designed for optimizing MGAT and the confederate image network, respectively. By integrating temporal and spatial perspectives, the proposed approach achieves greater robustness to outliers than approaches that consider only one perspective. The experimental results confirm the effectiveness of the proposed approach.

Improving plug-in hybrid electric vehicles (PHEVs) fuel economy requires a proper energy management strategy (EMS). Efforts to enhance the energy-saving performance of predictive EMSs have concentrated on advanced speed prediction methods. However, the impact of battery models on the predictive EMS hasn't been investigated. This paper aims to fill the research gap by suggesting a model predictive control-based (MPC) EMS framework that uses a hybrid speed predictor. Firstly, six control-oriented battery electro-thermal-aging models with various terminal voltage and temperature simulation accuracies have been developed and verified. Secondly, it also examines the effects of different battery models on MPC-based EMS through quantitative analysis, including battery dynamics, calculation time, and resultant operating costs, which leads to the suggestions of model selection for the design of the EMS under low- and room-temperature driving scenarios. Finally, a novel hybrid speed prediction model is proposed, where the historical speed is decomposed into strongly periodic intrinsic mode functions (IMFs) by variational mode decomposition (VMD), and backpropagation (BP) neural network is utilized to learn the feature parameter and mapping relationship of each IMF. In addition, a case study is conducted by applying the proposed speed prediction model in an MPC-based EMS method. The simulation results highlight that the proposed hybrid speed prediction model can achieve preferable speed prediction. The operating cost errors (compared with the MPC-based EMS with 100% speed prediction) are reduced to 0.4% and 0.98% at −20 °C and 25 °C driving scenarios, respectively.

Both the operational and ultimate load conditions should be considered in the structural design and reliability assessment of wind turbine systems. In the operational condition, the fatigue load experienced by wind turbine blades is of great concern in design which highly relies upon the rotor's rotation. Three kinds of methods have been developed to explore the rotational sampling effect of wind speeds on wind turbine blades, which, however, are somewhat inconvenient in practical applications. In view of the recent developments in wind field simulation, a novel rotational sampling method allowing for the analytical expression of fluctuating wind speeds on rotating blades is proposed in the present paper. In contrast to the existing methods, the proposed method circumvents the decomposition of cross power spectrum density (PSD) matrix and the interpolation in spatial and temporal dimensions. In particular, a closed-form expression of the rotational sampling spectrum is provided, thereby the mechanism of transfer of turbulent kinetic energy in frequency domain is quantitatively revealed. For illustrative purposes, fatigue analysis of the blades of a 5-MW offshore wind turbine is carried out, demonstrating the non-negligible influence of the rotational sampling on the fatigue load of blades and the competitive efficiency of the proposed method.

The operating flexibility of the power units is getting increasing attention from power systems especially those with large-scale fluctuating renewable energies. However, the combined heat and power (CHP) units are getting a bottleneck because their electricity productions are restricted by heat productions. This study aims to develop an electric-heat coordinated control strategy to make the CHP units more flexible. First of all, the dynamic model for a 300 MW CHP unit is set up, and its linear state-space description is obtained. A control strategy based on linear quadratic regulator (LQR) is then developed to satisfy different heat-power demands in various operating conditions. The control weights Q and R are optimized by particle swarm optimization. Moreover, the improved coordinated control strategy based on precise energy balance is put forward to increase the CHP power ramp rate considering electricity priority strategy and recovery control of the heat source. Finally, the simulation results show that the improved strategy is suitable for various CHP operating scenarios, and the case for electricity priority and heat recovery control significantly improves the unit power rate on the premise of stable heat supply. This work provides a reliable and flexible control mode for CHP units, which can support the power system stability and renewable energy integration.