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Abstract Battery remaining charging time (RCT) prediction can facilitate charging management and alleviate mileage anxiety for electric vehicles (EVs). Also, it is of great significance to improve EV users' experience. However, the RCT for a lithium-ion battery pack in EVs changes with temperature and other battery parameters. This study proposes an electrothermal model-based method to accurately predict battery RCT. Firstly, a characteristic battery cell is adopted to represent the battery pack, thus an equivalent circuit model (ECM) of the characteristic battery cell is established to describe the electrical behaviors of a battery pack. Secondly, an equivalent thermal model (ETM) of the battery pack is developed by considering the influence of ambient temperature, thermal management, and battery connectors in the battery pack to calculate the temperature which is then fed back to the ECM to realize electrothermal coupling. Finally, the RCT prediction method is proposed based on the electrothermal model and validated in the wide temperature range from − 20 ℃ to 45 ℃. The experimental results show that the prediction error of the RCT in the whole temperature range is less than 1.5%.

Lithium-ion batteries have become the best choice for battery energy storage systems and electric vehicles due to their excellent electrical performances and important contributions to achieving the carbon-neutral goal. With the large-scale application, safety accidents are increasingly caused by lithium-ion batteries. As the core component for battery energy storage systems and electric vehicles, lithium-ion batteries account for about 60% of vehicular failures and have the characteristics of the rapid spread of failure, short escape time, and easy initiation of fires, so the safety improvement of lithium-ion batteries is urgent. This study analyses the causes and mechanisms of lithium-ion batteries failures from design, production, and application, investigates its failure features and warning algorithms for thermal runaway, and the concept of long-medium-short graded warning is proposed based on the battery failure mechanism and its evolution to provide a basis for failure warning. As lithium-ion batteries fires are difficult to completely avoid, the characteristics of lithium-ion batteries fires are explored to improve battery structure and develop fire extinguishing agents and methods for fire prevention and suppression. Improving the safety of batteries is a systematic project, and at a time when there has been no breakthrough in the chemical system, improvements, such as build a practical graded warning system, are needed in all aspects of design, production, use and disposal to improve battery safety and minimize the risk of failure.

With rapid transportation electrification worldwide, lithium-ion batteries have gained much attention for energy storage in electric vehicles (EVs). State of power (SOP) is one of the key states of lithium-ion batteries for EVs to optimise power flow, thereby requiring accurate online estimation. Equivalent circuit model (ECM)-based methods are considered as the mainstream technique for online SOP estimation. They primarily vary in their basic principle, technical contribution, and validation approach, which have not been systematically reviewed. This paper provides an overview of the improvements on ECM-based online SOP estimation methods in the past decade. Firstly, online SOP estimation methods are briefed, in terms of different operation modes, and their main pros and cons are also analysed accordingly. Secondly, technical contributions are reviewed from three aspects: battery modelling, online parameters identification, and SOP estimation. Thirdly, SOP testing methods are discussed, according to their accuracy and efficiency. Finally, the challenges and outlooks are presented to inspire researchers in this field for further developments in the future.

Lithium-ion batteries (LIBs) have emerged as the preferred energy storage systems for various types of electric transports, including electric vehicles, electric boats, electric trains, and electric airplanes. The energy management of LIBs in electric transports for all-climate and long-life operation requires the accurate estimation of state of charge (SOC) and capacity in real-time. This study proposes a multi-stage model fusion algorithm to co-estimate SOC and capacity. Firstly, based on the assumption of a normal distribution, the mean and variance of the residual error from the model at different ageing levels are used to calculate the weight for the establishment of a fusion model with stable parameters. Secondly, a differential error gain with forward-looking ability is introduced into a proportional–integral observer (PIO) to accelerate convergence speed. Thirdly, a fusion algorithm is developed by combining a multi-stage model and proportional–integral–differential observer (PIDO) to co-estimate SOC and capacity under a complex application environment. Fourthly, the convergence and anti-noise performance of the fusion algorithm are discussed. Finally, the hardware-in-the-loop platform is set up to verify the performance of the fusion algorithm. The validation results of different aged LIBs over a wide range of temperature show that the presented fusion algorithm can realize a high-accuracy estimation of SOC and capacity with the relative errors within 2% and 3.3%, respectively.

Abstract With the wide deployment of rechargeable batteries, battery degradation prediction has emerged as a challenging issue. However, battery life defined by capacity loss provides limited information regarding battery degradation. In this article, we explore the prediction of voltage‐capacity curves over battery lifetime based on a sequence to sequence (seq2seq) model. We demonstrate that the data of one present voltage‐capacity curve can be used as the input of the seq2seq model to accurately predict the voltage‐capacity curves at 100, 200, and 300 cycles ahead. This offers an opportunity to update battery management strategies in response to the predicted consequences. Besides, the model avoids feature engineering and is flexible to incorporate different numbers of input and output cycles. Therefore, it can be easily transplanted to other battery systems or electrochemical components. Furthermore, the model features data generation, that is, we can use the data of only one cycle to generate a large spectrum of aging data at the future cycles for developing other battery diagnosis or prognosis methods. In this way, the time and energy consuming battery degradation tests can be sharply reduced.

Abstract A new adaptive scheme is proposed in this paper to design excitation controllers for feedback linearized models of synchronous generators in multimachine power systems in order to ensure the stability during large disturbances. The proposed scheme uses speed deviations of synchronous generators, readily available measured physical properties of multimachine power systems, to make all generators within a power network as partially linearized as well as to provide more damping. An adaptive scheme is then used to estimate all unknown parameters which appear in the partial feedback linearizing excitation controllers in order to avoid parameter sensitivities of existing feedback linearization techniques. The overall stability of multimachine power systems is ensured through the excitation control and parameter adaptation laws. The Lyapunov stability theory is used to theoretically analyse the stability of multimachine power systems with the proposed scheme. Simulation studies are presented to evaluate the performance of the proposed excitation control scheme for two different test systems by different operating conditions including short‐circuit faults on key locations along with variations in parameters for a large duration. Furthermore, comparative results are presented to highlight the superiority of the proposed adaptive partial feedback linearizing excitation control scheme over an existing partial feedback linearizing excitation controllers.

External short circuit (ESC) of lithium-ion batteries is one of the common and severe electrical failures in electric vehicles. In this study, a novel thermal model is developed to capture the temperature behavior of batteries under ESC conditions. Experiments were systematically performed under different battery initial state of charge and ambient temperatures. Based on the experimental results, we employed an extreme learning machine (ELM)-based thermal (ELMT) model to depict battery temperature behavior under ESC, where a lumped-state thermal model was used to replace the activation function of conventional ELMs. To demonstrate the effectiveness of the proposed model, we compared the ELMT model with a multi-lumped-state thermal (MLT) model parameterized by the genetic algorithm using the experimental data from various sets of battery cells. It is shown that the ELMT model can achieve higher computational efficiency than the MLT model and better fitting and prediction accuracy, where the average root mean squared error (RMSE) of the fitting is 0.65 °C for the ELMT model and 3.95 °C for the MLT model, and the RMES of the prediction under new data set is 3.97 °C for the ELMT model and 6.11 °C for the MLT model.

Abstract State of charge (SOC) estimation for lithium ion batteries plays a critical role in battery management systems for electric vehicles. Battery fractional order models (FOMs) which come from frequency-domain modelling have provided a distinct insight into SOC estimation. In this article, we compare five state-of-the-art FOMs in terms of SOC estimation. To this end, firstly, characterisation tests on lithium ion batteries are conducted, and the experimental results are used to identify FOM parameters. Parameter identification results show that increasing the complexity of FOMs cannot always improve accuracy. The model R(RQ)W shows superior identification accuracy than the other four FOMs. Secondly, the SOC estimation based on a fractional order unscented Kalman filter is conducted to compare model accuracy and computational burden under different profiles, memory lengths, ambient temperatures, cells and voltage/current drifts. The evaluation results reveal that the SOC estimation accuracy does not necessarily positively correlate to the complexity of FOMs. Although more complex models can have better robustness against temperature variation, R(RQ), the simplest FOM, can overall provide satisfactory accuracy. Validation results on different cells demonstrate the generalisation ability of FOMs, and R(RQ) outperforms other models. Moreover, R(RQ) shows better robustness against truncation error and can maintain high accuracy even under the occurrence of current or voltage sensor drift.

The depletion of fossil fuels, the increase of energy demands, and the concerns over climate change are the major driving forces for the development of renewable energy such as solar and wind. However, the intermittency of renewable energy has hindered the deployment of large-scale intermittent renewable energy, which therefore has necessitated the development of advanced large-scale energy storage technologies [item 1) in the Appendix]. The use of large-scale energy storage can effectively improve the efficiency of energy resource utilization and increase the use of variable renewable resources, energy access, and end-use sector electrification [items 2) and 3) in the Appendix]. Over the years, considerable research has been conducted on many types of energy, such as thermal energy, mechanical energy, electrical energy, and chemical energy, using different types of systems such as phase change materials, batteries, supercapacitors, fuel cells, and compressed air, which are applicable to various types of applications, such as heat and power generation and electrical/hybrid transportation [items 4) and 5) in the Appendix].

Pourthiaea is of great ornamental value because it produces white flowers in spring and summer, red fruit in autumn, and their fruit does not fall in winter. In order to explore the genetic structure and evolutionary characteristics of the chloroplast genome of Pourthiaea, comparative genomics analysis and phylogenetic analysis were conducted using ten published chloroplast genomes of Pourthiaea from the NCBI database. The results showed that the chloroplast genomes of the ten species of Pourthiaea showed typical circular tetrad structures, and the genome sizes were all within the range of 160,159–160,401 bp, in which the large single copy was 88,047–88,359 bp, the small single copy was 19,234–19,338 bp, and the lengths of a pair of inverted repeats were 26,341–26,401 bp. The GC contents ranged from 36.5% to 36.6%. A total of 1017 SSR loci were identified from the chloroplast genomes of the ten species of Pourthiaea, including six types of nucleotide repeats. The gene types and gene distribution of the IR boundary regions of the chloroplast genomes of different species of Pourthiaea were highly conservative, with little variation. Through the sequence alignment of chloroplast genomes, it was found that the chloroplast genomes of the ten species of Pourthiaea were generally highly conservative. The variation mainly occurred in the spacer regions of adjacent genes. Through nucleic acid diversity analysis, three hypervariable regions were screened at Pi > 0.006, namely trnQ(UUC)-psbk-psbl, accD-psal, and ndhF-rpl32-trnL (UAG). Phylogenetic analysis showed that the ten species of the genus Pourthiaea were clustered in the same branch and formed sister groups with the genus Stranvaesia, and that the support rate for the monophyly of the genus Pourthiaea was high. This study can serve as a reference for the breeding, genetic evolution, and phylogeny of Pourthiaea.

Rosa lucieae is one of the famous wild ancestors of cultivated roses and plays a very important role in horticultural research, but there is still a lack of research on the R. lucieae chloroplast genome. In this study, we used the Illumina MiSeq platform for sequencing, assembly, and annotation to obtain the R. lucieae chloroplast genome sequencing information and compared genomics, selection stress analysis, and phylogenetic analysis with 12 other chloroplast genomes of Rosa. The R. lucieae cpDNA sequence has a total length of 156,504 bp, and 130 genes are annotated. The length of all 13 studied chloroplast genomes is 156,333~157,385 bp. Their gene content, gene sequence, GC content, and IR boundary structure were highly similar. Five kinds of large repeats were detected that numbered 100~116, and SSR sequences ranged from 78 to 90 bp. Four highly differentiated regions were identified, which can be used as potential genetic markers for Rosa. Selection stress analysis showed that there was significant positive selection among the 18 genes. The phylogenetic analysis of R. lucieae and R. cymose, R. maximowicziana, R. multiflora, and R. pricei showed the closest relationship. Overall, our results provide a more comprehensive understanding of the systematic genomics and comparative genomics of Rosa.

Current studies on the mechanical abuse of lithium-ion batteries usually focus on the mechanical damage process of batteries inside a jelly roll. In contrast, this paper investigates the internal short circuits inside batteries. Experimental results of voltage and temperature responses of lithium-ion batteries showed that battery internal short circuits evolve from a soft internal short circuit to a hard internal short circuit, as battery deformation continues. We utilized an improved coupled electrochemical-electric-thermal model to further analyze the battery thermal responses under different conditions of internal short circuit. Experimental and simulation results indicated that the state of charge of Li-ion batteries is a critical factor in determining the intensities of the soft short-circuit response and hard short-circuit response, especially when the resistance of the internal short circuit decreases to a substantially low level. Simulation results further revealed that the material properties of the short circuit object have a significant impact on the thermal responses and that an appropriate increase in the adhesion strength between the aluminum current collector and the positive electrode can improve battery safety under mechanical abusive conditions.

This paper proposes a hierarchical optimization method for the energy scheduling of multiple microgrids (MMGs) in the distribution network of power grids. An energy market operator (EMO) is constructed to regulate energy storage systems (ESSs) and load demands in MMGs. The optimization process is divided into two stages. In the first stage, each MG optimizes the scheduling of its own ESS within a rolling horizon control framework based on a long-term forecast of the local photovoltaic (PV) output, the local load demand and the price sent by the EMO. In the second stage, the EMO establishes an internal price incentive mechanism to maximize its own profits based on the load demand of each MG. The optimization problems in these two stages are solved using mixed integer programming (MIP) and Stackelberg game theory, respectively. Simulation results verified the effectiveness of the proposed method in terms of the promotion of energy trading and improvement of economic benefits of MMGs.

Solar energy plays an important role in the global energy framework for future. Comparing with conventional generation systems using fossil fuels, the cost structure of photovoltaic (PV) systems is different: the capital cost is higher while the operation cost is negligible. Reliabilities of the PV system can also influence the cost for producing electricity. Investors, planners and regulators require deep insight into the return and cost of a PV project. A reliability based economical assessment of large-scale PV systems has been conducted utilizing Universal Generating Function (UGF) techniques. The reliability models of solar panel arrays, PV inverters and energy production units (EPUs) are represented as the corresponding UGFs. The expected energy production models for different PV system configurations have also been developed. The expected unit cost of electricity has been calculated to provide informative metrics for making optimal decisions. The proposed method has been applied to determine the PV system configuration which provides electricity for a water purification process.

This paper proposes a new high-precision rotor position measurement (RPM) method for permanent magnet spherical motors (PMSMs). In the proposed method, a LED light spot generation module (LSGM) was installed at the top of the rotor shaft. In the LSGM, three LEDs were arranged in a straight line with different distances between them, which were formed as three optical feature points (OFPs). The images of the three OFPs acquired by a high-speed camera were used to calculate the rotor position of PMSMs in the world coordinate frame. An experimental platform was built to verify the effectiveness of the proposed RPM method.

Modularity-in-design of battery packs for electric vehicles (EVs) is crucial to offset their high manufacturing cost. However, inconsistencies in performance of EV battery packs can be introduced by various sources. Sources of variation affect their robustness. In this paper, parameter diagram, a value-based conceptual analysis approach, is applied to analyze these variations. Their interaction with customer requirements, i.e., ideal system output, are examined and critical engineering features for designing modular battery packs for EV applications are determined. Consequently, sources of variability, which have a detrimental effect on mass-producibility of EV battery packs, are identified and differentiated from the set of control factors. Theoretically, appropriate control level settings can minimize sensitivity of EV battery packs to the sources of variability. In view of this, strength of the relationship between ideal system response and various control factors is studied using a “house of quality” diagram. It is found that battery thermal management system and packaging architecture are the two most influential parameters having the largest effect on reliability of EV battery packs. More importantly, it is noted that heat transfer between adjacent battery modules cannot be eliminated. For successful implementation of modular architecture, it is, therefore, essential that mechanical modularity must be enabled via thermal modularity of EV battery packs.

Abstract: Non-uniformity of Lithium-ion cells in a battery pack is inevitable and has become the bottleneck to the pack capacity, especially in the fast charging process. Therefore, a balancing approach is essentially required. This paper proposes an active online cell balancing approach in a fast charging process using the state of charge (SOC) as balancing criterion. The goal of this approach is to complete pack balancing within the limited charging time. An adaptive extended Kalman filter (AEKF) is applied to estimate the pack cell SOC during the charging process to obtain accurate results under modeling errors and measurement noises. To implement the proposed AEKF, only one additional current sensor is required to obtain the current of each cell required for the SOC estimation. An experimental platform is established to verify the effectiveness of the proposed approach. The results show that the proposed balancing approach with the SOC as a balancing criterion can overcome the challenges of non-uniformity and flat voltage plateau and charge more capacity into a LiFePO4 battery pack than those with the terminal voltage as a balancing criterion in the fast charging process.

Based on a low cost multi-switched inductor balancing circuit (MSIBC), a fuzzy logic (FL) controller is proposed to improve the balancing performances of lithium-ion battery packs instead of an existing proportional-integral (PI) controller. In the proposed FL controller, a cell’s open circuit voltages (OCVs) and their differences in the pack are used as the inputs, and the output of the FL controller is the balancing current. The FL controller for the MSIBC has the advantage of maintaining high balancing currents over the existing PI controller in almost the entire balancing process for different lithium battery types. As a result, the proposed FL controller takes a much shorter time to achieve battery pack balancing, and thus more pack capacity can be recovered. This will help to improve the pack performance in electric vehicles and extend the serving time of the battery pack.