Accurate estimation of the state of charge (SOC) and state of energy (SOE) is extremely difficult due to their sensitivity to operating conditions and complex coupling relationships. To address the problems, this paper proposes a co-estimation of SOC and SOE using model-data fusion. First, adaptive resampling and the Metropolis-Hastings algorithm are used to overcome the two core problems of particle filtering: particle degradation and sample depletion, achieving robust and accurate SOC estimation. Subsequently, a deep learning framework for SOE estimation is proposed, which includes feature extraction using two-dimensional convolutional neural network, importance enhancement using squeeze-and-excitation network attention mechanism, dependency capture using bidirectional long short-term memory neural network, and online correction using Gaussian filtering. Validation results at different temperatures demonstrate that the proposed method achieves maximum mean absolute error (MAE) and root mean square error (RMSE) of 0.806% and 1.144%, respectively, for SOC estimation, and converges to the true SOC value in just one time step. The SOE estimation shows a MAE of less than 0.827%, an RMSE of less than 1.005%, and a maximum error of less than 2.486%. The proposed hybrid model-data-driven co-estimation decouples the complex mapping relationship between parameters and improves accuracy by more than twice compared with the advanced methods in the field.

With the advent of Big Data, managing large-scale portfolios of thousands of securities is one of the most challenging tasks in the asset management industry. This study uses an evolutionary multi-objective technique to solve large-scale portfolio optimisation problems with both long-term listed and newly listed securities. The future returns of long-term listed securities are defined as random variables whose probability distributions are estimated based on sufficient historical data, while the returns of newly listed securities are defined as uncertain variables whose uncertainty distributions are estimated based on experts’ knowledge. Our approach defines security returns as theoretically uncertain random variables and proposes a three-moment optimisation model with practical trading constraints. In this study, a framework for applying arbitrary multi-objective evolutionary algorithms to portfolio optimisation is established, and a novel evolutionary algorithm based on large-scale optimisation techniques is developed to solve the proposed model. The experimental results show that the proposed algorithm outperforms state-of-the-art evolutionary algorithms in large-scale portfolio optimisation.

Achieving comprehensive and accurate detection of battery anomalies is crucial for battery management systems. However, the complexity of electrical structures and limited computational resources often pose significant challenges for direct on-board diagnostics. A multifunctional battery anomaly diagnosis method deployed on a cloud platform is proposed, meeting the needs of anomaly detection, localization, and classification. First, the proposed method extracts four anomaly features from discharge voltage to indicate battery anomalies. A risk screening process is applied to classify vehicles into high, medium, and low-risk categories with these features. Next, these classifications and prior anomaly labels are utilized in the offline phase to train an anomaly classifier. Then, the types of faults are further segmented by a specially developed voltage cumulative difference mean model, the warning information is refined. Finally, the proposed method was validated on data from 25 real vehicles, achieving an anomaly detection accuracy rate that exceeded 98%, demonstrating its accurate detection capability. This article proffers an effective multifunctional vehicle anomaly detection method, providing a new approach to assist in-vehicle fault diagnosis with the support of a reliable cloud computing foundation.

To guarantee the safety of electric vehicles (EVs), abusive operations should be strictly prohibited for EV-mounted li-ion batteries (LiBs). This article proposes an intelligent strategy to infer the historical abusive operations (HAOs) on LiBs by retrospectively analyzing the relationship between HAO-induced damages and electrical behaviors. First, the electrical synchronicity among the peer LiB cells in a series module is perceived using an improved correlation coefficient formula; then the synchronicity sequences are translated into recurrence plot images (RCPIs) and Gramian angular field images (GAFIs) that can provide a wealth of textures regarding cross-time autocorrelations. Second, based on the hierarchical clustering algorithm, a pilot test is performed to preliminarily examine the separability of these images corresponding to different HAOs. Finally, the GoogLeNet model is employed to model the causality between image features and HAO specifics, whereby the type and intensity of potential HAO can be inferred. A realistic dataset is obtained by inflicting abuses such as overvoltage, overheat, and vibration on LiB cells. Experimental verifications show that the proposed strategy performs well in backtracking the HAOs on LiBs by providing effective and reliable inferences on HAO specifics. The accuracy rates of HAO type assessment and severity evaluation can achieve about 77% and 76% using RCPIs, and about 79% and 76% using GAFIs, respectively.

Battery production line is crucial for determining the performance of batteries, further significantly affecting the industrial applications of relevant energy systems. As a complex and multidisciplinary system involving electrical, mechanical, and chemical processes, efficient prediction of manufactured battery properties and explainable analysis of strongly coupled battery production variables becomes an important but challenging issue for the wider application of batteries. In this article, an interpretable AI solution based on generalized additive model with interactive features and interpretability (GAM-IFI) is proposed to effectively predict battery capacities in the early phase of battery manufacturing and explain the effects of involved coating features. The designed solution is evaluated by using reliable production data from a real battery manufacturing line. Illustrative results show that the proposed solution is able to accurately predict three different types of battery capacities with an$R^{2}$over 0.98. Moreover, information regarding the importance ratio of both main effect and pairwise interaction terms derived from three coating features is identified, while global and local interpretations of the effects of these terms can be well explained. The developed interpretable solution opens a promising avenue to identify the importance of battery production features and explain how the variation of these features influences the properties of battery products. This can help engineers to better understand the underlying complex behaviors in battery production, which in turn will benefit the digitalization of battery manufacturing.

Given the exponential surge in wireless devices and data traffic, a key objective for forthcoming wireless communication systems lies in reducing energy consumption, thereby adhering to the emerging trend of green communication. Thus, devising an energy-efficient resource allocation scheme for Heterogeneous Networks (HetNets) is of utmost importance. In this paper, on the purpose of reducing energy consumption while ensuring users’ Quality of Service (QoS) requirements, we construct an energy-efficient optimization function for joint allocation of sub-channels, power and wireless backhaul bandwidth (JASPW) in NOMA-assisted HetNets with wireless backhaul considering imperfect channel state information (CSI), which poses non-convex and hybrid nonlinear challenge, providing an un-affordable computational complexity. Unlike the previous approach of decomposing the JASPW issue into several sub-convex problems, to tackle the problem efficiently, we first give the closed-form expressions through the derivation of the outage probability constraints, thus transforming the problem into a deterministic and convex one, subsequently, we propose a novel quantum-inspired equilibrium optimizer (QEO) algorithm to allocate the joint resource simultaneously, thereby obtaining the optimal resource allocation solution. Simulation results indicate that the proposed QEO yields an outstanding performance over other strategies in different communication scenarios.

Lithium-ion batteries are crucial power sources in many industries. When they are used at low temperatures, their performance decreases greatly. Thus, heating is required. This article proposes a health-aware heating strategy based on the ac current. The strategy combines the use of the electro-thermal model for predicting the temperature increment in the battery caused by the ac current and a diagnostic method for lithium plating based on the charging transfer impedance. To obtain an accurate electro-thermal model for ac heating, various equivalent circuit models (ECMs) are tested at pulses with different frequencies and amplitudes, and the second-order ECM with a constant phase element is found to obtain the smallest error. Then, the model and the lithium plating criterion are used to establish the boundary map of the lithium plating and voltage limitations. Based on the map, the optimal ac frequency and amplitude are selected for the heating strategy. Finally, an ac heating experiment is implemented to verify the strategy. The results show that the strategy not only increases the battery temperature at a maximum rate of 5.25 °C/min, but also achieves a capacity decrease of 0.3% in 80 heating cycles. Moreover, according to the battery disassembly results, there is no lithium plating on the electrode. These results prove that the proposed heating strategy can prevent lithium plating and achieve a high heating efficiency.

Lithium-ion batteries have increasingly become a primary energy source in Electric Vehicles (EVs), power grid energy storage, aerospace, and other fields. Accurate State of Charge (SOC) estimation is crucial for the safe and efficient operation of lithium batteries. This paper proposes a physical-data fusion framework for accurate SOC estimation of Lithium-ion batteries. First, a fractional-order model (FOM) of lithium-ion batteries is built to describe the electrochemical reactions inside the battery, and a fractional-order Extended Kalman Filter (FOEKF) algorithm is used to achieve preliminary SOC estimation. Second, the FOM is combined with an error model established by Long Short-Term Memory (LSTM) to improve the accuracy of the FOEK-based SOC estimation by compensating for the estimation error. Finally, the proposed framework is verified on Maryland dataset, and the experimental results demonstrate that the fusion framework exhibits superior performance in improving the accuracy of SOC estimation, with at least 75% and 50% reduction in MSE and max error, respectively.

The popularization of electric vehicles (EVs) and charging stations has been threatening the distribution network’s reliability and efficiency. The prediction of EV charging demand can benefit the optimization of the operation of energy-transportation nexus and improve social welfare toward a low carbon future. In this article, a short-term probabilistic charging demand forecast model is proposed to estimate the quantiles of future charging demand of a charging station 15 min ahead, i.e., the self-attention-based machine theory of mind (SAMToM). The SAMToM has considered both the users’ historical charging habits (schedules) and the current trend of charging demand variation using the framework of machine theory of mind (MToM), and real-world-data-based case studies have verified its superiority in EV charging demand forecast over state-of-the-arts. Moreover, analyses show that the advantage of SAMToM lies in the following aspects. 1) The self-attention layers have mitigated the long-range forgetting in SAMToM. 2) The MToM architecture enables SAMToM to balance historical charging habits and current charging demand variation trends well. 3) Using a quantile forecast evaluation metric as the loss function, i.e., the continuous ranked probability score (CRPS), enables SAMToM to aim directly at the highest quality of forecasted quantiles.

Battery-based energy storage system is a key component to achieve low carbon industrial and social economy, where battery health status plays a vital role in determining the safety and reliability of energy-transportation nexus. This article proposes a transferred recurrent neural network (RNN)-based framework to achieve efficient calendar capacity prognostics under both witnessed and unwitnessed storage conditions. Specifically, this transferred RNN framework contains a base model part and a transfer model part. The base model is first trained by using the easily collected and time-saving accelerated ageing dataset from high temperature and state-of-charge (SOC) cases. Then the transfer part is tuned by using only a small portion of starting capacity data from unwitnessed condition of interest. The developed framework is evaluated under a well-rounded ageing dataset with three different storage SOCs (20%, 50%, and 90%) and temperatures (10 °C, 25 °C, and 45 °C). Experimental results demonstrate that the derived transferred RNN framework is capable of providing satisfactory calendar capacity health prognostics under different storage cases. A model structure with the impact factor terms of SOC and temperature outperforms other counterparts especially for the unwitnessed conditions. The proposed framework could assist engineers to significantly reduce battery ageing experiment burden and is also promising to capture future capacity information for battery health and life-cycle cost analysis of energy-transportation applications.

Battery calendar aging prediction is of extreme importance for developing durable electric vehicles. This article derives machine learning-enabled calendar aging prediction for lithium-ion batteries. Specifically, the Gaussian process regression (GPR) technique is employed to capture the underlying mapping among capacity, storage temperature, and state-of-charge. By modifying the isotropic kernel function with an automatic relevance determination (ARD) structure, high relevant input features can be effectively extracted to improve prediction accuracy and robustness. Experimental battery calendar aging data from nine storage cases are utilized for model training, validation, and comparison, which is more meaningful and practical than using the data from a single condition. Illustrative results demonstrate that the proposed GPR model with ARD Matern32 (M32) kernel outperforms other counterparts and can achieve reliable prediction results for all storage cases. Even for the partial-data training test, multistep prediction test, and accelerated aging training test, the proposed ARD-based GPR model is still capable of excavating the useful features, therefore offering good generalization ability and accurate prediction results for calendar aging under various storage conditions. This is the first-known data-driven application that utilizes the GPR with ARD kernel to perform battery calendar aging prognosis.

Effective lithium-ion battery pack charging is of extreme importance for accelerating electric vehicle development. This article derives an optimal charging control strategy with a leader-followers framework for battery packs. Specifically, an optimal average state-of-charge (SOC) trajectory based on cells' nominal model is first generated through a multiobjective optimization with consideration of both user demand and battery pack's energy loss. Then, a distributed charging strategy is proposed to make the cells' SOCs follow the prescheduled trajectory, which can effectively suppress the violation of the safety-related charging constraints through online battery model bias compensation. This article highlights the superiorities of the proposed leader-followers-based charging framework that combines the offline scheduling and online closed-loop regulation for battery pack charging, which brings benefits to significantly reduce the computational burden for the charger controller as well as improve the robustness to suppress the negative impact caused by the cell's model bias. Extensive illustrative results demonstrate the effectiveness of the proposed optimal charging control strategy.

Self-heating is of extreme importance for improving the available capacity and lifetime of lithium-ion batteries in cold climates. However, few attempts have been done to achieve effective onboard self-heating for the batteries in electric vehicles. This paper derives a high-frequency sine-wave (SW) heater based on resonant LC converters to self-heat the automotive batteries at low-temperatures without the need of external heaters. To be specific, an interleaved-parallel topology is introduced to double the heating speed without extra damages to batteries compared to the single heater. Further, a corresponding thermoelectric model is developed to provide guidance for the optimal design of the parameters in the proposed SW heater. Experimental results show that with a high-frequency sinusoidal current motivated by the proposed heater, lithium-ion batteries could be effectively self-heated by the ohmic-loss and electrochemical heat. Moreover, the heating time could be significantly shortened through decreasing the characteristic impedance √(L/C) or increasing the ac-heating frequency.

This paper applies advanced battery modeling and multiobjective constrained nonlinear optimization techniques to derive suitable charging patterns for lithium-ion batteries. Three important yet competing charging objectives, including battery health, charging time, and energy conversion efficiency, are taken into account simultaneously. These optimization objectives are first subject to a high-fidelity battery model that is synthesized from recently developed individual electrical, thermal, and aging models. The coupling relationship and multiple timescales among different model dynamics are identified. Furthermore, constraints are imposed explicitly on the current, voltage, state-of-charge, and temperature. Such a complex charging problem is solved by using an ensemble multiobjective biogeography-based optimization approach. As a result, two charging patterns, namely the constant current-constant voltage (CC-CV) and multistage CC-CV, are optimized to balance various combinations of charging objectives. Different tradeoffs and sensitive elements are compared and analyzed based on the Pareto frontiers. Illustrative results demonstrate that the proposed strategy can effectively offer feasible health-conscious charging with desirable tradeoffs among charging speed and energy conversion efficiency under different demand priorities.

Battery charging strategy is a key issue in battery management system to ensure good battery performance and safe operation during the charging process. In this paper, a novel battery optimal charging strategy is proposed by applying the TLBO algorithm to a LiFeP04 battery for an optimal charging based on a coupled thermoelectric model. A specific dual-objective function including battery charging time and temperature rise (both battery interior and surface) is formulated first. Then a battery optimal charging strategy is presented in detail by using the TLBO algorithm, aiming at finding a suitable constant-current-constant-voltage (CCCV) current profile to minimize the dual-objective function. Besides, the effects of different weights in dual-objective function on the optimal charging profile are analyzed. Simulation results demonstrate that the presented optimal charging strategy can provide effective and acceptable optimal charge current profile. The strategy can be also easily implemented to other battery types to effectively balance the battery charging time and battery temperature rise during charging process.