The increasing number of electric vehicles (EVs) on the roads has led to a rise in the number of batteries reaching the end of their first life. Such batteries, however, still have a capacity of 75–80% remaining, creating an opportunity for a second life in less power-intensive applications. Utilising these second-life batteries (SLBs) requires specific preparation, including grading the batteries based on their State of Health (SoH); repackaging, considering the end-use requirements; and the development of an accurate battery-management system (BMS) based on validated theoretical models. In this paper, we conduct a technical review of mathematical modelling and experimental analyses of SLBs to address existing challenges in BMS development. Our review reveals that most of the recent research focuses on environmental and economic aspects rather than technical challenges. The review suggests the use of equivalent-circuit models with 2RCs and 3RCs, which exhibit good accuracy for estimating the performance of lithium-ion batteries during their second life. Furthermore, electrochemical impedance spectroscopy (EIS) tests provide valuable information about the SLBs’ degradation history and conditions. For addressing calendar-ageing mechanisms, electrochemical models are suggested over empirical models due to their effectiveness and efficiency. Additionally, generating cycle-ageing test profiles based on real application scenarios using synthetic load data is recommended for reliable predictions. Artificial intelligence algorithms show promise in predicting SLB cycle-ageing fading parameters, offering significant time-saving benefits for lab testing. Our study emphasises the importance of focusing on technical challenges to facilitate the effective utilisation of SLBs in stationary applications, such as building energy-storage systems and EV charging stations.

Ultra-wideband (UWB) sensors have been widely applied to indoor positioning. The indoor positioning of UWB sensors usually refers to the positioning of the mobile node that interacts with the anchors through radio for calculating the distance between the mobile node and each of the surrounding anchors. The positioning accuracy of the mobile node is affected by the installation positions of surrounding anchors. A mathematical model was proposed in this paper to respectively analyze the mobile node’s 2-dimensional (2D) and 3-dimensional (3D) positioning errors. The factors influencing the mobile node’s positioning errors were explored through the mathematical models. The best installation positions of surrounding anchors were obtained based on the mathematical models. The mobile node’s 2D and 3D positioning errors were reduced based on the anchor positions derived from the mathematical model. Both computer simulations and practical experiments were implemented to justify the results obtained in the mathematical models.

To enable efficient data gathering in wireless sensor networks, a novel partial matrix completion algorithm is proposed by introducing historical data to the original data matrix. The recovery accuracy is further improved by making full use of the band-limited feature of the sensory data. We analyzed the recovery error of the proposed algorithm and derived a theoretical error upper bound. Experimental results on two real-world data sets show that the proposed algorithm achieves good recovery accuracy even when the sampling ratio is very low, which outperforms the state-of-the-art methods.

An innovative solution to provide a demand response for power system is proposed in this paper by considering the feasibility of two-way direct load control (TWDLC) of central air conditioning chiller system for wide-area in real-time manner. Particularly, the proposed TWDLC scheme is designed to tackle the load shedding ratio optimization problem for all under-controlled customers, aiming to satisfy the target load curtailment defined in each scheduling step. Another notable contribution of this work is the utilization of constraint loosening concept on actual load, curtailed to overcome the uncertainties of load reduction during TWDLC. Given the presence of fuzzy constraints, the proposed load shedding ratio optimization problem can be tackled using fuzzy linear programming. A delicate strategy is then formulated to transform the proposed fuzzy linear programming problem into a regular linear programming problem. A selection scheme used to obtain the feasible candidates set for load shedding at every sampling interval of TWDLC is also designed along with the fuzzy linear programming.

This paper presents a novel and robust 3D localisation approach that tightly integrates ultra-wideband, inertial measurement unit, and magnetometer data to achieve high-precision attitude and 3D position estimation. In contrast to existing methods, which often fail to account for the impacts of significant translational motion or are only effective under small linear accelerations, this work introduces a linear acceleration compensation method that dynamically adjusts measurement uncertainties in real-time. This method addresses a critical limitation in previous approaches, enabling higher accuracy during significant motion. The proposed method leverages two extended Kalman filters (EKFs): six degrees of freedom EKF for attitude estimation and nine degrees of freedom EKF for 3D position estimation, which are coupled for enhanced performance. The experimentally validated results show that the proposed method significantly outperforms state-of-the-art techniques, achieving root mean square errors of 1.96 degrees for attitude estimation and 0.57 m for position estimation, while reducing computational complexity by 58.32%. This approach excels in challenging indoor environments with mixed line-of-sight and non-line-of-sight conditions, demonstrating its robustness and applicability to real-world scenarios. This work presents a significant advancement in the field of 3D localisation, offering both theoretical contributions and practical improvements over existing systems.