Flexible bidirectional pulse charging regulation achieving long-life lithium-ion batteries

Typical application scenarios,such as vehicle to grid(V2G)and frequency regulation,have imposed significant long-life demands on lithium-ion batteries.Herein,we propose an advanced battery life-extension method employing bidirectional pulse charging(BPC)strategy.Unlike traditional constant current charging methods,BPC strategy not only achieves comparable charging speeds but also facilitates V2G frequency regulation simultaneously.It significantly enhances battery cycle ampere-hour throughput and demonstrates remarkable life extension capabilities.For this interesting conclusion,adopting model identification and postmortem characterization to reveal the life regulation mechanism of BPC:it mitigates battery capacity loss attributed to loss of lithium-ion inventory(LLI)in graphite anodes by intermittently regulating the overall battery voltage and anode potential using a negative charging current.Then,from the perspective of internal side reaction,the life extension mechanism is further revealed as inhibition of solid electrolyte interphase(SEI)and lithium dendrite growth by regulating voltage with a bidirectional pulse current,and a semi-empirical life degradation model combining SEI and lithium dendrite growth is developed for BPC scenarios health management,the model parameters are identified by genetic algorithm with the life simulation exhibiting an accuracy exceeding 99%.This finding indicates that under typical rate conditions,adaptable BPC strategies can extend the service life of LFP battery by approximately 123%.Consequently,the developed advanced BPC strategy offers innovative perspectives and insights for the development of long-life battery applications in the future.

Effect of preload forces on multidimensional signal dynamic behaviours for battery early safety warning

Providing early safety warning for batteries in real-world applications is challenging.In this study,comprehensive thermal abuse experiments are conducted to clarify the multidimensional signal evolution of battery failure under various preload forces.The time-sequence relationship among expansion force,voltage,and temperature during thermal abuse under five categorised stages is revealed.Three characteristic peaks are identified for the expansion force,which correspond to venting,internal short-circuiting,and thermal runaway.In particular,an abnormal expansion force signal can be detected at temperatures as low as 42.4°C,followed by battery thermal runaway in approximately 6.5 min.Moreover,reducing the preload force can improve the effectiveness of the early-warning method via the expansion force.Specifically,reducing the preload force from 6000 to 1000 N prolongs the warning time(i.e.,227 to 398 s)before thermal runaway is triggered.Based on the results,a notable expansion force early-warning method is proposed that can successfully enable early safety warning approximately 375 s ahead of battery thermal runaway and effectively prevent failure propagation with module validation.This study provides a practical reference for the development of timely and accurate early-warning strategies as well as guidance for the design of safer battery systems.

Specialized deep neural networks for battery health prognostics:Opportunities and challenges

Lithium-ion batteries are key drivers of the renewable energy revolution,bolstered by progress in battery design,modelling,and management.Yet,achieving high-performance battery health prognostics is a significant challenge.With the availability of open data and software,coupled with automated simulations,deep learning has become an integral component of battery health prognostics.We offer a comprehensive overview of potential deep learning techniques specifically designed for modeling and forecasting the dynamics of multiphysics and multiscale battery systems.Following this,we provide a concise summary of publicly available lithium-ion battery test and cycle datasets.By providing illustrative examples,we emphasize the efficacy of five techniques capable of enhancing deep learning for accurate battery state prediction and health-focused management.Each of these techniques offers unique benefits.(1)Transformer models address challenges using self-attention mechanisms and positional encoding methods.(2) Transfer learning improves learning tasks within a target domain by leveraging knowledge from a source domain.(3) Physics-informed learning uses prior knowledge to enhance learning algorithms.(4)Generative adversarial networks(GANs) earn praise for their ability to generate diverse and high-quality outputs,exhibiting outstanding performance with complex datasets.(5) Deep reinforcement learning enables an agent to make optimal decisions through continuous interactions with its environment,thus maximizing cumulative rewards.In this Review,we highlight examples that employ these techniques for battery health prognostics,summarizing both their challenges and opportunities.These methodologies offer promising prospects for researchers and industry professionals,enabling the creation of specialized network architectures that autonomously extract features,especially for long-range spatial-temporal connections across extended timescales.The outcomes could include improved accuracy,faster training,and enhanced generalization.

An ultra-fast charging strategy for lithium-ion battery at low temperature without lithium plating

Conventional charging methods for lithium-ion battery(LIB)are challenged with vital problems at low temperatures:risk of lithium(Li)plating and low charging speed.This study proposes a fast-charging strategy without Li plating to achieve high-rate charging at low temperatures with bidirectional chargers.The strategy combines the pulsed-heating method and the optimal charging method via precise control of the battery states.A thermo-electric coupled model is developed based on the pseudo-twodimensional(P2D)electrochemical model to derive charging performances.Two current maps of pulsed heating and charging are generated to realize real-time control.Therefore,our proposed strategy achieves a 3 C equivalent rate at 0℃ and 1.5 C at-10℃ without Li plating,which is 10–30 times faster than the traditional methods.The entropy method is employed to balance the charging speed and the energy efficiency,and the charging performance is further enhanced.For practical application,the power limitation of the charger is considered,and a 2.4 C equivalent rate is achieved at 0℃ with a 250 kW maximum power output.This novel strategy significantly expands LIB usage boundary,and increases charging speed and battery safety.

In-depth investigation of the exothermic reactions between lithiated graphite and electrolyte in lithium-ion battery

Thermal runaway is a critical issue for the large application of lithium-ion batteries.Exothermic reactions between lithiated graphite and electrolyte play a crucial role in the thermal runaway of lithium-ion batteries.However,the role of each component in the electrolyte during the exothermic reactions with lithiated graphite has not been fully understood.In this paper,the exothermic reactions between lithiated graphite and electrolyte of lithium-ion battery are investigated through differential scanning calorimetry(DSC) and evolved gas analysis.The lithiated graphite in the presence of electrolyte exhibit three exothermic peaks during DSC test.The reactions between lithiated graphite and LiPF_(6) and ethylene carbonate are found to be responsible for the first two exothermic peaks,while the third exothermic peak is attributed to the reaction between lithiated graphite and binder.In contrast,diethylene carbonate and ethyl methyl carbonate contribute little to the total heat generation of graphite-electrolyte reactions.The reaction mechanism between lithiated graphite and electrolyte,including the major reaction equations and gas products,are summarized.Finally,DSC tests on samples with various amounts of electrolyte are performed to clarify the quantitative relationship between lithiated graphite and electrolyte during the exothermic reactions.2.5 mg of lithiated graphite (Li_(0.8627)C_(6)) can fully react with around 7.2 mg electrolyte,releasing a heat generation of 2491 J g^(-1).The results presented in this study can provide useful guidance for the safety improvement of lithium-ion batteries.

Parameter-independent error correction for potential measurements by reference electrode in lithium-ion batteries

The safety monitoring of lithium-ion batteries(LIBs) is of great significance for realizing all-climate and full-lifespan battery management. In-situ measurement of anode potential with implanted reference electrodes(REs) has proven to be effective to monitor and avoid the occurrence of severe side reactions like Li plating to ensure the safe and fast charging. However, the intrinsic measurement errors caused by local blocking effects, which also can be referred to as potential artefacts, are seldom taken into consideration in existing studies, yet they highly dominate the correctness of conclusions inferred from REs. In this study, aiming at exploring the physical origin of the measurement errors and ensure reliable potential monitoring, electrochemical and post-mortem tests are conducted using commercial pouch cells with implanted REs. Corresponding electrochemical model which describes the blocking effects, is established to validate the abnormal absence of lithium plating that predicted by measured anode potentials under various charging rates. Theoretical derivation is further presented to explain the error sources, which can be attributed to increased local liquid potential of the RE position. Most importantly, with the guidance of error analysis, a novel parameter-independent error correction method for RE measurements is proposed for the first time, which is proven to be adequate to estimate the real anode potentials and deduce the critical C-rate of Li plating with extra safety margin. After error correction, the resulting critical C-rates are all within the range of 0.55 ± 0.03 C, which is close to the C-rate of 0.6–0.7 C obtained from experiments. In addition, this error correction method can be performed conveniently with only some simple RE measurements of polarization voltages, totally independent of battery electrochemical and geometric parameters. This study provides highly practical error correction method for RE measurements in real LIBs, substantially facilitating the fast diagnosis and safety evaluation of Li plating during charging of LIBs.

Regulation of dietary fiber on intestinal microorganisms and its effects on animal health

The animal gut harbors diverse microbes that play an essential role in the well-being of their host.Specific diets,such as those rich in dietary fiber,are vital in disease prevention and treatment because they affect intestinal flora and have a positive impact on the metabolism,immunity,and intestinal function of the host.Dietary fiber can provide energy to colonic epithelial cells,regulate the structure and metabolism of intestinal flora,promote the production of intestinal mucosa,stimulate intestinal motility,improve glycemic and lipid responses,and regulate the digestion and absorption of nutrients,which is mainly attributed to short-chain fatty acids(SCFA),which is the metabolite of dietary fiber.By binding with G protein-coupled receptors(including GPR41,GPR43 and GPR109A)and inhibiting the activity of histone deacetylases,SCFA regulate appetite and glucolipid metabolism,promote the function of the intestinal barrier,alleviate oxidative stress,suppress inflammation,and maintain immune system homeostasis.This paper reviews the physicochemical properties of dietary fiber,the interaction between dietary fiber and intestinal microorganisms,the role of dietary fiber in maintaining intestinal health,and the function of SCFA,the metabolite of dietary fiber,in inhibiting inflammation.Furthermore,we consider the effects of dietary fiber on the intestinal health of pigs,the reproduction and lactation performance of sows,and the growth performance and meat quality of pigs.

Thermal Runaway Characteristics and Modeling of LiFePO4 Power Battery for Electric Vehicles

LiFePO_(4)(LFP)lithium-ion batteries have gained widespread use in electric vehicles due to their safety and longevity,but thermal runaway(TR)incidents still have been reported.This paper explores the TR characteristics and modeling of LFP batteries at different states of charge(SOC).Adiabatic tests reveal that TR severity increases with SOC,and five stages are identified based on battery temperature evolution.Reaction kinetics parameters of exothermic reactions in each TR stage are extracted,and TR models for LFP batteries are established.The models accurately simulate TR behaviors at different SOCs,and the simulated TR characteristic temperatures also agree well with the experimental results,with errors of TR characteristic temperatures less than 3%.The prediction errors of TR characteristic temperatures under oven test conditions are also less than 1%.The results provide a comprehensive understanding of TR in LFP batteries,which is useful for battery safety design and optimization.

A Comparative Study of Charging Voltage Curve Analysis and State of Health Estimation of Lithium-ion Batteries in Electric Vehicle

Lithium-ion(Li-ion)cells degrade after repeated cycling and the cell capacity fades while its resistance increases.Degra-dation of Li-ion cells is caused by a variety of physical and chemical mechanisms and it is strongly influenced by factors including the electrode materials used,the working conditions and the battery temperature.At present,charging voltage curve analysis methods are widely used in studies of battery characteristics and the constant current charging voltage curves can be used to analyze battery aging mechanisms and estimate a battery’s state of health(SOH)via methods such as incremental capacity(IC)analysis.In this paper,a method to fit and analyze the charging voltage curve based on a neural network is proposed and is compared to the existing point counting method and the polynomial curve fitting method.The neuron parameters of the trained neural network model are used to analyze the battery capacity relative to the phase change reactions that occur inside the batteries.This method is suitable for different types of batteries and could be used in battery management systems for online battery modeling,analysis and diagnosis.

Role of dietary amino acids and microbial metabolites in the regulation of pig intestinal health

With the rapid development of sequencing technology,research on pigs has focused on intestinal microbes.Accumulating evidence suggests that the metabolites of intestinal microbes are the key medium for interactions between microbes and the host.Amino acid metabolism is involved in the growth and immune processes of pigs.The gut microbes of pigs are heavily involved in the metabolism of amino acids in their hosts.Here,we review the latest relevant literature.Research findings show that microbial metabolites,such as indoles,short-chain fatty acids,and ammonia,play a key role in gut health.Moreover,we summarize the effects of amino acids on the structure of the gut microbial community and the metabolism of amino acids by pig gut microbes.Evidence shows that microbial amino acid metabolites act as signal molecules in the intestine and play an important role in the intestinal health of pigs.