In the past five years, there have been numerous cases of Li-ion battery fires and explosions, resulting in property damage and bodily injuries. This paper discusses the thermal runaway mechanism and presents various thermal runaway mitigation approaches, including separators, flame retardants, and safety vents. The paper then overviews measures for extinguishing fires, and concludes with a set of recommendations for future research and development.

A state-of-charge (SOC) versus open-circuit-voltage (OCV) model developed for batteries should preferably be simple, especially for real-time SOC estimation. It should also be capable of representing different types of lithium-ion batteries (LIBs), regardless of temperature change and battery degradation. It must therefore be generic, robust and adaptive, in addition to being accurate. These challenges have now been addressed by proposing a generalized SOC-OCV model for representing a few most widely used LIBs. The model is developed from analyzing electrochemical processes of the LIBs, before arriving at the sum of a logarithmic, a linear and an exponential function with six parameters. Values for these parameters are determined by a nonlinear estimation algorithm, which progressively shows that only four parameters need to be updated in real time. The remaining two parameters can be kept constant, regardless of temperature change and aging. Fitting errors demonstrated with different types of LIBs have been found to be within 0.5%. The proposed model is thus accurate, and can be flexibly applied to different LIBs, as verified by hardware-in-the-loop simulation designed for real-time SOC estimation.

The implementation of multistep direct model predictive control (MPC) for dc-dc boost converters overcomes the well-known issue of nonminimum phase behavior. However, it can lead to a high computational burden depending on the prediction horizon length. In this paper, a simple and computationally efficient MPC method for dc-dc boost converters is proposed. The key novelty of the presented control strategy lies in the way dynamic references are handled. The control strategy is capable of providing suitable references for the inductor current and the output voltage, without requiring additional control loops. Moreover, this reference design allows the predictive controller to be implemented with a single-step prediction horizon. Thus, a significant reduction in the required real-time calculations executed in the control hardware is achieved. To obtain constant switching frequency, the power switch commutation instants within a sampling period are considered as control inputs. Therefore, the predictive controller is formulated as a continuous control set MPC. Additionally, the proposed formulation is able to deal with different operation modes of the converter without changing the controller structure. Finally, an observer is used to dynamically modify the reference to provide robustness to system parameter uncertainties. Simulation and experimental results show an accurate tracking of dynamic inductor current and output voltage references, while respecting the restrictions on maximum inductor current levels of the converter.

A thermal runaway happened in a battery cell of an electric vehicle during driving, and the fire spreads to other batteries in a few minutes. Based on the recorded battery operation data, this paper analyzes the battery thermal runaway process, and identifies the critical time points of fault signals like cell voltages and temperatures, etc., which depict the start and spread of thermal runaway in the battery pack. By tracing back the clues, it is concluded that the cause of the fire is battery internal short circuit (ISC), and battery overcharge is a potential cause of ISC. Then, three different patterns of battery cell voltage abnormalities are compared, including battery external short circuit, internal short circuit, and poor signal wire connection. A cell voltage difference-based method is proposed to diagnose ISC online, which is simple and reliable to implement in engineering.

A hybrid method for the prediction of the remaining useful life (RUL) of Lithium-ion batteries considering error-correction is proposed in respect of capacity diving phenomenon in later capacity degradation. First, an improved empirical capacity degradation model is proposed based on our previous work, with the analytic expression further revised in this paper to enhance its fitting accuracy, parameter identifiability, and applicability to tracking algorithm. Unscented particle filter (UPF) algorithm is then implemented to obtain prognostic results to produce original error series. Next, to enhance the quality of original error data by reducing the uncertainty, complete ensemble empirical mode decomposition (CEEMD) algorithm is utilized to reconstruct error series. The fundamental error evolution information is retained by selecting relatively highly correlated intrinsic mode functions (IMFs) of the decomposition results of original error series. Finally, after employing Gaussian process regression (GPR) algorithm, the prognostic error to correct the UPF-based prognostic results is obtained from the reconstructed error series. RUL prediction experiments for batteries with different working conditions have been conducted to verify the improved performance of the proposed model and the hybrid method, with mean absolute percentage errors of battery capacity degradation predicted less than 0.4%.

Due to the widespread deployment of distributed energy resources (DERs) and the liberalization of electricity market, traditional distribution networks are undergoing a transition to active distribution systems (ADSs), and the traditional deterministic planning methods have become unsuitable under the high penetration of DERs. Aiming to develop appropriate models and methodologies for the planning of ADSs, the key features of ADS planning problem are analyzed from the different perspectives, such as the allocation of DGs and ESS, coupling of operation and planning, and high-level uncertainties. Based on these analyses, this comprehensive literature review summarizes the latest research and development associated with ADS planning. The planning models and methods proposed in these research works are analyzed and categorized from different perspectives including objectives, decision variables, constraint conditions, and solving algorithms. The key theoretical issues and challenges of ADS planning are extracted and discussed. Meanwhile, emphasis is also given to the suitable suggestions to deal with these abovementioned issues based on the available literature and comparisons between them. Finally, several important research prospects are recommended for further research in ADS planning field, such as planning with multiple micro-grids (MGs), collaborative planning between ADSs and information communication system (ICS), and planning from different perspectives of multi-stakeholders.