Accurate health estimation and lifetime prediction of lithium-ion batteries are crucial for durable electric vehicles. Early detection of inadequate performance facilitates timely maintenance of battery systems. This reduces operational costs and prevents accidents and malfunctions. Recent advancements in "Big Data" analytics and related statistical/computational tools raised interest in data-driven battery health estimation.Here, we will review these in view of their feasibility and cost-effectiveness in dealing with battery health in real-world applications. We categorise these methods according to their underlying models/algorithms and discuss their advantages and limitations. In the final section we focus on challenges of real-time battery health management and discuss potential next-generation techniques. We are confident that this review will inform commercial technology choices and academic research agendas alike, thus boosting progress in datadriven battery health estimation and prediction on all technology readiness levels.

Batteries have been widely applied in many high-power applications, such as electric vehicles (EVs) and hybrid electric vehicles, where a suitable battery management system (BMS) is vital in ensuring safe and reliable operation of batteries. This paper aims to give a brief review on several key technologies of BMS, including battery modelling, state estimation and battery charging. First, popular battery types used in EVs are surveyed, followed by the introduction of key technologies used in BMS. Various battery models, including the electric model, thermal model and coupled electro-thermal model are reviewed. Then, battery state estimations for the state of charge, state of health and internal temperature are comprehensively surveyed. Finally, several key and traditional battery charging approaches with associated optimization methods are discussed.

Predicting future capacities and remaining useful life (RUL) with uncertainty quantification is a key but challenging issue in the applications of battery health diagnosis and management. This paper applies advanced machine-learning techniques to achieve effective future capacities and RUL prediction for lithium-ion batteries with reliable uncertainty management. To be specific, after using the empirical mode decomposition (EMD) method, the original battery capacity data is decomposed into some intrinsic mode functions (IMFs) and a residual. Then the long short term memory (LSTM) sub-model is applied to estimate the residual while the gaussian process regression (GPR) sub-model is utilized to fit the IMFs with the uncertainty level. Consequently, both the long-term dependence of capacity and uncertainty quantification caused by the capacity regenerations can be captured directly and simultaneously. Experimental aging data from different batteries are deployed to evaluate the performance of proposed LSTM+GPR model in comparison with the solo GPR, solo LSTM, GPR+EMD and LSTM+EMD models. Illustrative results demonstrate the combined LSTM+GPR model outperforms other counterparts and is capable of achieving accurate results for both 1-step and multi-step ahead capacity predictions. Even predicting the RUL at the early battery cycle stage, the proposed data-driven approach still presents good adaptability and reliable uncertainty quantification for battery health diagnosis.

p53, circRNAs and miRNAs are important components of the regulatory network that activates the EMT program in cancer metastasis. In prostate cancer (PCa), however, it has not been investigated whether and how p53 regulates EMT by circRNAs and miRNAs. Here we show that a Amotl1-derived circRNA, termed circAMOTL1L, is downregulated in human PCa, and that decreased circAMOTL1L facilitates PCa cell migration and invasion through downregulating E-cadherin and upregulating vimentin, thus leading to EMT and PCa progression. Mechanistically, we demonstrate that circAMOTL1L serves as a sponge for binding miR-193a-5p in PCa cells, relieving miR-193a-5p repression of Pcdha gene cluster (a subset of the cadherin superfamily members). Accordingly, dysregulation of the circAMOTL1L-miR-193a-5p-Pcdha8 regulatory pathway mediated by circAMOTL1L downregulation contributes to PCa growth in vivo. Further, we show that RBM25 binds directly to circAMOTL1L and induces its biogenesis, whereas p53 regulates EMT via direct activation of RBM25 gene. These findings have linked p53/RBM25-mediated circAMOTL1L-miR-193a-5p-Pcdha regulatory axis to EMT in metastatic progression of PCa. Targeting this newly identified regulatory axis provides a potential therapeutic strategy for aggressive PCa.