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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 ± 0.03C, which is close to the C-rate of 0.6–0.7C 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.

Although energy densities of lithium-ion batteries (LIBs) continue to increase, safety problems such as fires and explosions have significantly hindered their large-scale applications. Conventional wisdom tells us the fire of LIBs largely originated from the flammable liquid carbonate solvents, and thus the research on additives with properties of suppressing “liquid-type fire” is the way to fabricate nonflammable batteries. However, the outcome has been proven to be inferior. Here, we carefully examined the evolution of battery fire under thermal abuse condition and found that the “jet fire” caused by flammable gases inside batteries is critical for the fire hazard, and the research effort should be redirected to search proper additives with “gaseous-type fire” suppressing properties. Unfortunately, conventional “gaseous-type fire suppressant” shows poor compatibility with battery electrolytes. Thus, we rationally designed a new kind of supramolecular electrolyte, where the molecules of “gaseous-type fire suppressant” and “SEI&CEI improver” are self-assembled together by intermolecular interactions. Due to this design, the supramolecular electrolyte produces improved cycling stability of Li metal anode and high voltage LiCoO2 cathode (up to 4.4 V). More importantly, it sharply decreases the self-extinguish time of carbonate electrolyte from ∼100 s/g to ∼0 s/g, which is unprecedented and can effectively suppress the “jet fire”, thus preventing the further combustion of commercial pouch cell during thermal runaway. Our work is providing the criteria required to overcome this challenge via supramolecular engineering of the electrolyte, which provides a new way to tune electrolyte functions and may bring numerous new possibilities.

Internal short circuit (ISC) is the major failure problem for the safe application of lithium-ion batteries, especially for the batteries with high energy density. However, how to quantify the hazard aroused by the ISC, and what kinds of ISC will lead to thermal runaway are still unclear. This paper investigates the thermal-electrical coupled behaviors of ISC, using batteries with Li(Ni1/3Co1/3Mn1/3)O2 cathode and composite separator. The electrochemical impedance spectroscopy of customized battery that has no LiPF6 salt is utilized to standardize the resistance of ISC. Furthermore, this paper compares the thermal-electrical coupled behaviors of the above four types of ISC at different states-of-charge. There is an area expansion phenomenon for the aluminum-anode type of ISC. The expansion effect of the failure area directly links to the melting and collapse of separator, and plays an important role in further evolution of thermal runaway. This work provides guidance to the development of the ISC models, detection algorithms, and correlated countermeasures.

Cascaded thermal runaway (TR) propagation is the utmost safety issue for large-format lithium-ion battery (LIB) modules because of the high risk of system fires or explosions. However, quenching TR without side effects still remains a challenge. Herein, we delivered an ultrathin smart firewall concept for avoiding the TR propagation in a LIB module. We demonstrate that the firewalls have thermally-triggered switchable thermal physical properties because of the synergistic effect of non-flammable phase change materials (NFPCM) and flexible silica nanofiber mats. Under TR condition, the firewalls give rise to multiple functions simultaneously, including cooling, fire extinguishing and thermal insulation. Consequently, the TR propagation between fully charged 50 Ah LIBs, with a transient thermal shock of up to 53 kW, is successfully suppressed by 1-mm-thick smart firewalls, yielding a maximum cell-to-cell temperature gap of 512 °C. The smart firewall design provides a reliable approach to quench TR propagation in large-format LIBs, which can also be suitable for other dynamically adaptive thermal-protection applications for oil tanks, space exploration, and firefighting equipment.

Layered Ni-rich LiNixMnyCo1-x-yO2 (NMC) materials are the most promising cathode materials for Li-ion batteries due to their favorable energy densities. However, the low thermal stability typically caused by detrimental oxygen release leads to significant safety concerns. Determining the pathways of oxygen evolution reaction is essential, as the ideal safety countermeasure is to break the reaction chain of thermal runaway. In this study, we demonstrate that two endogenous pathways of oxygen involved in strong exothermic reactions lead the NMC811|graphite pouch cell to an uncontrollable state, and we quantify the individual contribution of the pathways to thermal runaway. Approximately 41% of thermal-induced oxygen reacts aggressively with ethylene carbonate (EC) at the cathode/electrolyte interface with 16% heat generation, accelerating the self-heating rate and thereby further triggering thermal runaway. The residual oxygen that survives the reaction with carbonate spreads to the lithiated anode with major heat generation (65%), bringing the battery to the maximum destructive temperature during thermal runaway. By confirming the significant roles of EC and anode, a deeper understanding on battery fire was achieved. The revealed mechanism can help guide studies on stopping the two reaction pathways, allowing for the safer use of high-energy lithium-ion batteries in the future.

Accompanied by the adoption of aggressive cathodes to continuously improve batteries energy density, enhancing their safety is becoming increasingly urgent for the electric vehicle development. In-situ controllable formation of robust cathode-electrolyte interphase (CEI) with high inorganic content seems to be the most promising strategy to address the thermal runaway concerns. Moreover, the in-situ formation strategy via suitable electrolyte replacement or electrolyte additives is extremely simple yet effective, especially for industrial manufacture of batteries. Here, this paper briefly reviews recent advanced CEIs formed by conventional carbonate-based electrolytes, fluorinated electrolytes, concentrated electrolytes, and solid state electrolytes. The focus on the thermal stability of the cathodes after CEI modification, and at the same time conduct safety tests at the material, cell, and module levels for comprehensive evaluation are encouraged. The review will provide inspiration for future developments in battery safety and push forward the practical applications of newly developed high-energy density batteries.

During the usage of electric vehicles, the battery decays and the cell variations expand in the battery pack. In the discharge process, both the low-capacity cell and the micro-short-circuit (MSC) cell have the abnormal feature that the state-of-charge (SOC) differences increase continuously. Hence, a low-capacity cell is likely to be misdiagnosed as an MSC cell, and vice versa. In this article, a fault identification approach based on mutual information is proposed to detect the MSC cell and low-capacity cell. A decision tree for fault identification is established by analyzing the battery fault characteristics of the short circuit, low capacity, and the abnormality of initial SOC difference. It is pointed out that the SOC deviation of the low-capacity cell is related to the mean SOC, while that of the MSC cell is related to time. A low-pass filter is used to get internal resistance differences in order to achieve the SOC deviations based on the cell different model. Finally, the MSC cell and the low-capacity cell can be identified using the mutual information which can quantitatively calculate the correlation between the SOC deviation and the mean SOC. Experimental results prove that the proposed method is reliable to identify the MSC cell and the low-capacity cell.

Safety concerns are the main obstacle to large-scale application of lithium-ion batteries (LIBs), and thus, improving the safety of LIBs is receiving global attention. Within battery systems, the internal short circuit (ISC) is considered to be a severe hazard, as it may result in catastrophic safety failures, such as thermal runaway. Considering this, we provide a comprehensive review on the mechanism and evolutionary process of ISC, including modeling and simulation experiments and the methods of detection and diagnosis. First, ISC types and the inducing mechanism under various inducements are analyzed, and the evolution process of ISC is divided into three stages according to electrical and thermal characteristics. Second, eleven existing ISC substitute experimental methods are listed in detail, and three coupling models of electric-thermal-ISC models are introduced to simulate the characteristics of ISC. Third, existing ISC detection methods are reviewed in detail, divided into six categories. Moreover, we propose methods for ISC detection under four special conditions: ISC detection for the cells before grouping, ISC detection method during electric vehicle dormancy, ISC detection based on equilibrium electric quantity compensation to address negative impact of the equalization function of the battery management system on ISC detection, and effective fault tree diagnosis for the joint identification and early warning of multiple battery faults. Fourth, existing ISC prevention methods are reviewed. Finally, the key technologies of ISC are prospected, revealing that the development of big data and artificial intelligence technology will promote the accuracy and timeliness of ISC detection.

Summary Lithium iron phosphate (LiFePO4) batteries have been dominant in energy storage systems. However, it is difficult to estimate the state of charge (SOC) and safety early warning of the batteries. To solve these problems, this paper developed a multiple timescale comprehensive early warning strategy based on the consistency deviation of the electrical and thermal characteristics of LiFePO4 batteries. The unscented Kalman filter method was employed to estimate the battery SOC. The established comprehensive early warning strategy was verified through fault-triggered experiments at different time scales with different equivalent resistances. The results show that the comprehensive early warning strategy can realize early warning for different timescale failures of LiFePO4 batteries under different energy storage conditions. For more dangerous severe failures that can break the safety valve, safety early warning can be realized 15 min in advance. This study provides a reference to ensure safe and reliable operations of energy storage systems., Graphical abstract, Highlights • A comprehensive early warning strategy for multiple timescales was developed • The battery electric-thermal characteristics of different time scales were obtained • The proposed warning strategy expanded the application of early warning algorithms, Energy Resources; Energy systems; Energy management; Energy storage

Lithium-ion (Li-ion) cells degrade after repeated cycling and the cell capacity fades while its resistance increases. Degradation 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.

Battery safety, at the foundation of fast charging, is critical to the application of lithium-ion batteries, especially for high energy density cells applied in electric vehicles. In this paper, an earlier thermal runaway of cells after fast charging application is illustrated. Under this condition, the reaction between the plated lithium and electrolyte is revealed to be the mechanism of thermal runaway triggering. The mechanism is proved by the accelerated rate calorimetry tests for partial cells, which determine the triggering reactions of thermal runaway in the anode-electrolyte thermodynamic system. The reactants in this system are analyzed by nuclear magnetic resonance and differential scanning calorimetry, proving that the vigorous exothermic reaction is induced by the interaction between the plated lithium and electrolyte. As a result, the finding of thermal runaway triggered by the plated lithium on anode surface of cells after fast charging promotes the understanding of thermal runaway mechanisms, which warns of the danger of plated lithium in the utilization of lithium-ion batteries.

Real-time battery capacity estimation is very important for the battery management but usually has a low accuracy in electric vehicles due to the complicated real working conditions and the changing parameters during the battery lifespan. Traditional estimation methods, e.g. methods based on the empirical models such as the Arrhenius capacity aging model, or methods based on the state of charge, always suffer from the parameters mismatch during the long battery lifespan. In this paper, we put forward a method based on charging curve sections which can be easily achieved for electric vehicles. The proposed method uses the complete charging curves and the corresponding capacities in experiments as the training data for a certain battery type. The optimal fixed voltage window is then determined by the particle swarm optimization with a designed objective function focused on minimizing the error of linear capacity loss assumption. The capacity is finally estimated by calculating the charging capacities during the optimal fixed voltage window online. The proposed method is verified using the designed experimental data, and the error is proved to be small.

High-power charging (HPC) has been associated with a great potential to shorten the charging time, relative to increasing the all-electric range (AER) of battery electric cars (BECs). Such promise of applicability is however restrained by setbacks attributed to the high-voltage system of BECs, its negative influence on the battery performance, and a higher requirement of charging infrastructures. More importantly, the core issue is the functionality of the battery, as research has found that HPC may cause a high charging rate on the battery cell that consequently promotes its heat generation and eventually results in a significant loss of functionality and safety degradation. Considering these elements, the thermal reaction of a battery under HPC is seemingly the key toward the design of an effective thermal management system. Thus, we conducted the present study with a focal objective of examining the thermal reaction of a traction battery to the HPC, to evaluate the gap between existing battery technologies and HPC to find the suitable condition. We investigated the heat generation characteristics of Nickel–Cobalt–Manganese (NCM) and lithium iron phosphate (LFP) batteries by HPC testing in non-adiabatic and adiabatic conditions, respectively, for different charging rates from 0.5 to 5.0C. We analyzed and incorporated the test results to establish a model of battery heat generation, which could predict temperature rise under specified conditions. Moreover, we proposed an HPC operating boundary by considering the present battery technologies. The findings herein indicate that the increased charging rate leading to higher heat generation and lower charge efficiency can be effectively handled through specific thermal management, which, with the boundary, provides a limit for the optimal operation region to avoid safety risk. More practically, a good thermal management system is required to control the boundary setting and extend the operation region of battery charging.

The online estimation of battery state-of-health (SOH) is an ever significant issue for the intelligent energy management of the autonomous electric vehicles. Machine-learning based approaches are promising for the online SOH estimation. This paper proposes a machine-learning based algorithm for the online SOH estimation of Li-ion battery. A predictive diagnosis model used in the algorithm is established based on support vector machine (SVM). The support vectors, which reflects the intrinsic characteristics of the Li-ion battery, are determined from the charging data of fresh cells. Furthermore, the coefficients of the SVMs for cells at different SOH are identified once the support vectors are determined. The algorithm functions by comparing partial charging curves with the stored SVMs. Similarity factor is defined after comparison to quantify the SOH of the data under evaluation. The operation of the algorithm only requires partial charging curves, e.g., 15 min charging curves, making fast on-board diagnosis of battery SOH into reality. The partial charging curves can be intercepted from a wide range of voltage section, thereby relieving the pain that there is little chance that the driver charges the battery pack from a predefined state-of-charge. Train, validation, and test are conducted for two commercial Li-ion batteries with Li(NiCoMn)1/3O2 cathode and graphite anode, indicating that the algorithm can estimate the battery SOH with less than 2% error for 80% of all the cases, and less than 3% error for 95% of all the cases.

Optimal algorithms for energy management and component sizing play essential roles in reducing energy consumption and prolonging fuel cell durability of fuel cell electric vehicles. These two problems are usually coupled together, and it is of great importance to study. In this work, an energy consumption and durability model of a bus with a combined fuel cell, battery, and supercapacitor powertrain are introduced. A two-dimensional (2D) dynamic programing (DP) algorithm was designed to minimize energy consumption and system degradation. A real-time energy management strategy based on a 2D Pontryagin's Minimal Principle (2DPMP) was then proposed. Simulation results indicated that the proposed 2DPMP strategy can approximate the optimal strategy of dynamic programming well and showed a reduction in energy consumption and an increase in durability over other strategies. Component parameters and energy management strategy were simultaneously optimized based on the 2DPMP strategy, thus providing a set of optimal powertrain parameters on a Pareto front. Design rules for energy management strategy and component sizing were concluded from the set of optimal solutions.

Lithium-ion batteries experiences impedance rise and therefore power fade during aging. Current understanding toward the impedance rise is yet qualitative, leaving quantification of multiple contributions a challenging gap. To fill in this gap, we combine the distribution of relaxation times method and physics-based modeling to analyze the electrochemical impedance spectroscopy of lithium-ion batteries aged by cycling at 45 °C. We find that the oft-neglected low-frequency diffusion is the largest sole source to the impedance rise. Furthermore, thanks to the advanced physics-based impedance model, we distinguish electrolyte- and solid-phase diffusion, identifying that the former dominates over the latter. This piece of understanding implies novel insights into improving battery lifetime, and the methodology developed is flexible to other battery chemistries.

The need for a quick yet informative technique for diagnosing the lithium-ion batteries is escalating. Conventional impedance-based diagnosis methods are usually time-demanding for a complete electrochemical impedance spectroscopy measurement and involve complicated calculations to extract battery information, which therefore have limited applications in battery monitoring. In this study, we propose a multi-point impedance technique, involving impedance measurement on three characteristic frequency points and being able to separate ohmic, contact and solid electrolyte interphase resistances. The characteristic frequency points are calibrated using distribution of relaxation time method. This multi-point impedance technique holds potential for large-scale high-throughput battery monitoring and screening.

The lithium ion batteries are having increasing energy densities, meeting the requirement from industry, especially for the electric vehicles. However, a cell with a higher energy density is more prone to thermal runaway. We analyze the key characteristics during thermal runaway to help better define battery thermal runaway. Three characteristic temperatures are regarded as the common features of thermal runaway for all kinds of lithium ion batteries. The underlying mechanisms for the three characteristic temperatures have been investigated by thermal analysis. The conclusion of the analysis set benchmarks for evaluating the thermal runaway behaviors of commercial lithium-ion batteries, and the proposed methodologies benefits further research and development of battery safety design for electric vehicles.

Overcharge is one of the most severe safety issues of lithium-ion batteries. In this paper, the overcharge performance of a commercial lithium-ion battery is evaluated under different test conditions, considering the effects of charging current, restraining plate and heat dissipation. Charging current and heat dissipation are found to have only minor influence on the battery overcharge behaviors, while batteries with restraining plate and cuts on the pouches show improved overcharge performance, as SOCTR and TTR both increase significantly. Further analysis on TTR helps to identify the overcharge-induced thermal runaway mechanism for the batteries with different configurations.

A suitable model structure and matched model parameters are prerequisites for the precise estimation of the battery states. Previous studies pay little attention to whether a parameter identification method is suitable for a model. In this study, a comparative study is conducted by implementing model parameter optimization for nine equivalent circuit models using nine optimizers in the entire SOC area. The following conclusions are drawn: (1) PNGV and the exact algorithms are an ideal combination in the low SOC area (0%–20%). (2) In the high SOC area (20–100%), exact algorithms are an ideal choice for the first-order RC models, and PSO is an ideal identification algorithm for second-order RC models. For the third- and fourth-order RC models, firefly algorithm has the highest accuracy with longer identification time. (3) Firefly algorithm has the superior capacity to identify the accurate model parameters and PSO has the best comprehensive performance for on-line parameter identification.

Lithium plating leads to severe capacity fading and possible safety problems in lithium-ion batteries (LiB). Therefore, it is necessary to provide an in-situ detection methodology of lithium plating evolution during battery cycling. Qualitative analysis of the Distribution of Relaxation Times (DRT), based on Electrochemical Impedance Spectroscopy (EIS) deconvolution, shows a different degradation mechanism between lithium plating and normal aging batteries. A Support Vector Machine (SVM) detection algorithm based on the DRT is proposed. It only relies on the measured capacity and EIS data during battery aging, which is also easy to process and non-destructive to battery sample. The method has a significant advantage in battery safety management and echelon selection of retired batteries.

This paper presents a DC microgrid semi-decentralized control strategy for electric vehicle fast charging and hydrogen production. The equivalent circuit models of the photovoltaics (PV), battery energy storage system and the proton exchange membrane water electrolyzer (PEMWE) are built and integrated into an equivalent bus capacity model of the microgrid, where both model accuracy and computation efficiency are promised. The microgrid, excepted for the PEMWE, is always maintained under the decentralized mode, which is based on the Virtual battery model and Bus-signaling control. The control of the PEMWE is divided into the decentralized mode and power-based mode, the mode choice of which is determined by the PEMWE controller according to time period when the price of the grid is the lowest of the day. The PEMWE could utilize the extra energy generated by the PV array to stabilize the bus voltage, the daily production target could also be satisfied. The proposed semi- empirical approach is verified in the MATLAB/Simulink environment and the feasibility and priority of which is proved.

Liquid hydrogen provides a prospective solution for the on-board hydrogen storage and supply system of heavy-duty fuel cell vehicles to carry more hydrogen in a smaller volume. The research group develops 32-ton and 49-ton heavy-duty vehicles equipped with fuel cell systems, batteries, liquid hydrogen storage and supply systems, and independent-drive electric wheels. In this study, the two heavy-duty vehicles’ powertrain dynamic model based on experimental data is established. The maximum cruising ranges of two vehicles under constant-speed conditions and China-worldwide transient vehicle cycles (C-WTVCs) are then analyzed, respectively. The dynamic programming algorithm is adopted to find the optimal power distribution between the fuel cell system and the battery. Simulation results show that the 40-km/h cruising range of the 32-ton and 49-ton vehicles can be up to 1283 km and 1717 km with 62.0 kg and 112.2 kg hydrogen depleted completely and battery state of charge sustained. Dynamic programming results show that two vehicles’ cruising range can be 615.3 km and 768.7 km under consecutive C-WTVCs.

Fuel cell/battery passive hybrid power systems have the potential of saving energy due to the avoidance of DC/DC power loss. However, their energy-saving effect has not been investigated in previous research. This paper studies the fuel economy of a fuel cell/battery passive hybrid system for commercial vehicles through simulation. The energy consumption of the passive hybrid configuration is compared to that of a conventional active configuration under high-speed static conditions. The results show that the passive configuration can save energy by 3%-12% under the selected condition and energy management strategy (EMS). The energy-saving percentage increases as the efficiency of the fuel cell engine increases. When the initial SOC is lower than the sustaining target of the EMS, the energy-saving effect becomes more significant. This study confirms the energy-saving effect of the fuel cell passive hybrid system.

As the rapid development of renewable energy and electrification of transport, the stability and efficiency of power system will encounter inevitable challenges. A decentralized Vehicle-to-Grid (V2G) framework for fast charging infrastructures joining in the primacy frequency regulation is proposed in this paper to cope the fluctuation of renewable energies. Two interaction modes are transferred according to the state of charge (SOC) of PEVs (Plug-in electric vehicles) in order to meet the fast-charging demand of drivers primarily. Based on the engineering system of vehicle battery pack, we fabricate detailed interaction logics among fast charger, V2G controller, battery pack and battery management system (BMS). The effective V2G control strategies need to consider the power limitations and charging effects. As a consequence, an equivalent circuit battery model (ECM) with high accuracy for dynamic and fluctuation working conditions is employed to obtain power limitations of lithium-ion batteries (LIBs). Furthermore, fast-charging-oriented droop control strategies are designed to participate regulation of power grid with negligible impact on fast charging. An alternative current (AC) power system with high penetration of renewable resources is constructed to verify the effects. The results of case study reveal that the proposed method with bidirectional chargers can reduce the fluctuation from renewables, loads and PEVs by 21.22%, accompanied by PEVs’ SOC changes lower than 0.5%. Interestingly, a high renewable penetration system can eliminate frequency derivation by adding PEVs with proposed regulation.

Lithium plating is the key safety and capacity fade issue in lithium-ion battery. Here, we report a method using kinetic Monte Carlo simulation to simulate the dendrite growth process during lithium plating under over-charging, fast charging and low-temperature charging conditions. The morphology of lithium dendrite is tree-like, showing obvious tip attraction. The lithium plating number, average dendrite length, dendrite distribution coefficient, dendrite height and dendrite density are proposed to describe the lithium dendrite quantitatively. Finally, it is proved that pulse charging can effectively inhibit the lithium plating and dendrite growth.

According to the thermal ignition theory, the fire triangle (i.e. combustible, oxidizer, and ignition source) of lithium-ion battery (LIB) eruption gases, which are caused by thermal runaway, is key to causing LIB fires. The three fire boundaries corresponding to the fire triangle are the minimum battery eruption gases (BEG) concentration (cBEG, ignition), minimum oxygen concentration (cO2, ignition) and lowest temperature required for ignition. The purpose of this paper is to theoretically clarify the three fire boundaries of BEG. The BEG identification results of 29 thermal runaway tests in an inert atmosphere in open literature were summarized, and a BEG time-sequence diagram was drawn. According to Le Chatelier's mixing rule, the physical properties of gases and the thermal ignition theory, the three fire boundaries were theoretically analyzed for batteries with four types of cathode materials (i.e. LixCoO2 (LCO), LixFePO4 (LFP), LixNiyCozAl1-y-zO2 (NCA), and LixNiyCozMn1-y-zO2 (NMC)) at different state of charge (SOC) (0%~143%). The results showed that both of cBEG, ignition and cO2, ignition had different trends for different types of batteries with the increase in the SOC values at discharged states, but they were almost unchanged in the full and overcharged states. For the four types of batteries, from low to high, cBEG, ignition was NMC < LCO < NCA < LFP, and cO2, ignition was NCA < LFP < NMC < LCO. By controlling the SOC and/or selecting a reasonable battery type, cBEG, ignition and/or cO2, ignition could be changed, thereby changing the probability of battery fire. Batteries were prone to forced-ignition with forced-ignition sources regardless of the BEG temperature. When the eruption gases of a battery are at the eruption temperature without forced-ignition sources, the battery is prone to auto-ignition. However, the battery is hard to auto-ignited when the BEG temperature is cooled below the minimum self-ignition point (about 260 ℃) of the BEG components. The BEG ignition mode can be controlled by changing its temperature and ignition sources.These results can be used to guide battery pack design, thermal management system design and fire safety protection.

The development of distributed energy storage systems is an important guarantee for renewable energy into the grid. Considering life, cost and safety factors, lithium iron phosphate (LiFePO4) batteries have always been the dominant energy storage battery in energy storage systems. However, due to the relatively flat open circuit voltage platform of the LiFePO4 batteries, the estimation of battery state of charge (SOC) and safety early warning of battery is difficult. To solve these problems, a multiple timescale comprehensive early warning strategy based on the consistency deviation of the electrical and thermal characteristics is established. The equivalent circuit model (ECM) and the unscented Kalman filter (UKF) method are used to estimate battery SOC. The established comprehensive early warning strategy is verified through fault trigger experiments of different time scale through different equivalent resistance. Experiment results show that comprehensive early warning strategy can realize early warning of different time scale failures of LiFePO4 batteries under energy storage conditions. For more dangerous serious failures that cause safety valve broken, the safety early warning can be carried out 15 minutes in advance. This research can provide a reference for ensuring the safe and reliable operation of energy storage system.

Summary Heating battery at low temperatures is fundamental to avoiding the range anxiety and the time-consuming charging associated with electric vehicles (EVs). One method for achieving fast and uniform battery heating is to polarize the cell under pulse currents. However, the on-board implementation of this method leads to an increase in the cost and size. Therefore, in this study, an adapted EV circuitry compatible with the existing one and an optimized operating condition are proposed to enable rapid battery heating. With this circuit, electricity transfer between the cells can be realized through a motor, leading to remarkably higher battery currents than those of the conventional circuit. The increase in the maximum heating currents (from 1.41C to 4C) resulted in a battery temperature rise of 8.6°C/min at low temperatures. This heating method exhibits low cost, high efficiency, and negligible effects on battery degradation, practical and promising on battery heating of EVs., Graphical abstract, Highlights • Rapid temperature rise, high efficiency, and low cost for battery heating are obtained • The batteries are divided into two modules and connected to the inverter individually • The electricity transferred between battery modules is motivated by a static motor • The pulse currents for battery heating are improved significantly at any frequency, Electrochemistry; Electrochemical Energy Storage; Energy Engineering; Energy Systems; Energy Materials

This paper presents a novel dual-layer framework to find the most economic photovoltaic and battery capacity combination of a grid-connected DC microgrid. The system cost during the whole lifetime is addressed in the optimization framework through the net present cost based objective function, where the investment cost, battery replacement cost and operating and management cost are taken into account. The proposed CAPN model, which combines all of the battery ageing stressing factors, is incorporated to estimate the replacement number of the battery. A particle swarm optimization algorithm, which is initialized with a rule-based power dispatch strategy, is put forward in the energy management level to achieve the optimal power dispatch. In order to adapt to variant operating conditions, the ambient and electric vehicle charging profiles throughout a whole year are employed as the input conditions of the optimization problem. A 61.8k RMB reduction is realized with the optimal configuration under the proposed method compared with the rule-based energy management strategy, indicating the superior of the proposed optimization methodology. Besides, the optimal power dispatch is also implemented with the extra power fed back to the utility grid.

In this paper, we propose a multi-objective optimization problem of power components parameters for a pre-defined driving cycle and energy management algorithm regarding fuel economy, system durability, power components cost and battery volume. We eliminate the parameter vectors that can’t meet the vehicle power demand. Then applying the algorithm to the model of a fuel cell city bus gives the opportunity to choose the best values for battery capacity Q bat and the maximal net output power of the FCS P fcsmax according to the contour diagram. Simulation results show that for a "New Europe Driving Cycle", a battery capacity of 96 Ah and an FCS maximal net output power of 100 kW are optimal for the fuel economy, system durability, power components cost and battery volume limitation of a fuel cell city bus.

Concentrated electrolytes usually demonstrate good electrochemical performance and thermal stability, and are also supposed to be promising when it comes to improving the safety of lithium-ion batteries due to their low flammability. Here, we show that LiN(SO2F)2-based concentrated electrolytes are incapable of solving the safety issues of lithium-ion batteries. To illustrate, a mechanism based on battery material and characterizations reveals that the tremendous heat in lithium-ion batteries is released due to the reaction between the lithiated graphite and LiN(SO2F)2 triggered thermal runaway of batteries, even if the concentrated electrolyte is non-flammable or low-flammable. Generally, the flammability of an electrolyte represents its behaviors when oxidized by oxygen, while it is the electrolyte reduction that triggers the chain of exothermic reactions in a battery. Thus, this study lights the way to a deeper understanding of the thermal runaway mechanism in batteries as well as the design philosophy of electrolytes for safer lithium-ion batteries., Concentrated electrolytes display superior thermal stability due to their non-flammability nature. Here, the authors show that LiN(SO2F)2-based concentrated electrolytes are incapable of solving the safety issues due to heat release during reaction between the lithiated graphite and electrolyte.

The use of reduced-order electrochemical models creates opportunities for battery management systems to control the battery behavior by monitoring the internal states in electrochemical processes, which are critical for safety enhancement and degradation mitigation. This paper explores a state observer for lithium-ion batteries based on an extended single-particle model, which results in a trade-off between high accuracy and low computational burden, thus enables the real-time application. An adaptive unscented Kalman filter based on this model is developed to estimate not only the state of charge but also lithium-ion concentrations and potentials, which precisely describe battery internal behaviors to avoid lithium plating. Experimental tests are carried out with a lithium-ion battery cell for both model and state estimation validations. Furthermore, the estimation accuracies of the unmeasurable states are also verified by numerical validation tests with a high-fidelity electrochemical model. All estimated states present fast convergence, robustness, and high accuracy despite a 20% initial state-of-charge error. Additionally, the enhancement in the state estimation accuracy and robustness by the new noise adaption step is demonstrated by an application-relevant evaluation framework, considering sensor noise, state uncertainty, parameter uncertainty, and computation time.

Thermal runaway and its propagation are bottlenecks for the safe operation of lithium-ion battery systems. This study investigates the influence of characteristic thermophysical parameters during battery thermal runaway, such as the self-heating temperature (T1), triggering temperature (T2), mass loss, and critical heat transfer power (Pc), on the failure propagation behavior in a battery system. A parametric study is conducted based on a failure propagation model. This model not only captures the behavior of thermal failure, but also accounts for the changes in the thermophysical parameters before and after thermal runaway. The results of the modeling analysis demonstrate that increasing T1 and T2 can both delay the thermal runaway propagation. The delay achieved by increasing T2 is greater than that observed by increasing T1. The peak heat transfer power Pc plays a critical role in delaying the thermal runaway propagation. When the peak heat transfer power level is greater than Pc, thermal runaway propagation mainly results from heat transfer, whereas when the peak heat transfer power level is less than Pc, thermal runaway propagation mainly arises from self-heating. This study reveals the dynamic mechanism of thermal runaway propagation within a battery module, thus providing guidance for the safety design of battery systems.

The temperature of the battery jet is one of the key basic parameters for the design of battery thermal management system for vehicles, with sufficient results under combustion conditions in the presence of air. However, the original temperature distribution of battery jet in an inert atmosphere and its variation with the state of charge are not very clear for prismatic Ni-rich automotive batteries. This is closer to the real inside environment of the battery pack. In this work, a 50 Ah commercial prismatic cell with a Li(Ni0.6Mn0.2Co0.2)O2 cathode is triggered to a thermal runaway using external heating in a sealed chamber with a nitrogen atmosphere to avoid combustion caused by oxygen from the outside. The results show that the farther away from the safety valve, the lower the temperature of the jet. The jet temperature and its rise rate show an increasing trend with the maximum value of 701℃ and 173℃/s detected with increasing states of charge. Therefore, the BTMS design needs to take into account the high thermal load and high thermal shock caused by thermal runaway even in the absence of external air to participate in the combustion.

Sub-zero temperature causes performance degradation, lifespan shortage, and even some safety issues of Li-ion battery cells, such as the internal short circuit. Preheating has become a critical issue for electric vehicle (EV) promotion in the high-latitude area or cold temperatures. To address this issue, a double-direction liquid heating-based Cell-to-Chassis (CTC) battery module is proposed for an extreme low-temperature environment (-40°C). Two cooling plates and the battery module are embedded in the chassis to reduce the component number. Besides, the volume energy density gets increased by 26.3%. The numerical calculation indicates that the proposed system is more efficient than the commonly utilized battery thermal management system (BTMS) in EVs. Moreover, the preheating effect with different heating intervals is compared; eight-minutes-preheating is proved to be more effective in preheating the battery module to 0°C with less energy consumption. Furthermore, a multi-objective optimization design is further carried out considering the heating rate, thermal safety, thermal uniformity, and energy cost. The impact of the mass flow rate in the mini-channels and the PTC heating film power are analysed through sensitivity analysis. Finally, the optimal design scheme is selected. The minimum, maximum, and volume average temperatures of the battery module are 273.2K, 296.7K, and 286.9K, respectively. Moreover, the temperature standard deviation is further reduced to 8.8K without much energy cost increment. This study guides for combing the efficient BTMS with the EV chassis for all-climate applications, especially with integrated preheating/cooling functions, which is feasible for the fast charging and cold environment applications, both the thermal management efficiency and the volume energy density can be effectively enhanced.

Lithium-ion batteries (LIBs) are suffering from severe thermal runaway risks in the use of their whole lifespans. The heat release characteristics of thermal runaway after fast charging have been proven to be highly related to lithium plating, yet whose impacts on the eruption behaviors are rarely investigated. In this study, the changes in the battery eruption temperature during thermal runaway after fast charging are thoroughly analyzed, and the effects of lithium plating on gas production are revealed. Accelerating Rate Calorimetry tests of pouch cells and prismatic cells are performed to investigate the eruption temperature of LIBs charged at different rates, confirming the advanced eruption of thermal runaway on batteries with plated lithium. To reveal the root cause of early eruptions, reactions between plated lithium and electrolytes are characterized by Synchronous Thermogravimetry Analysis and Mass Spectrometry, observing the fierce gas production process. Afterwards, and the gas and solid products of the reaction are further obtained using partially reactive systems in hot-box tests, and their compositions are analyzed. Overall, this study contributes to a more profound understanding of the characteristics of thermal runaway after fast charging, providing valuable insights on the rational design and management for LIB safety.

Long, large-format lithium-ion batteries have become prominent in recent years in high-power application scenarios, such as in electrochemical energy storage stations, electric vehicles, and electric ships. In these batteries, failure is always initiated from a local point and then propagates to the full cell, requiring countermeasures to quench the in-cell thermal runaway propagation. This study investigates the thermal runaway propagation behaviors of long, large-format lithium-ion batteries. A thermal runaway front exists during the propagation of thermal runaway; it separates the failure zone and normal zone and carries significant information regarding the thermal runaway reactions. The characteristics of the thermal runaway front are investigated through experiments and simulations. The thermal runaway front moves forward with an average velocity of approximately 24.14 mm·s−1, driven by the large temperature gradient between the failure and intact zones. The velocity of the thermal runaway front is correlated with the thermophysical properties of the battery. A modeling analysis indicates that the velocity of the thermal runaway front in propagation has a square root correlation with the thermal conductivity and heat generation rate. This square root correlation links the failure process and the thermophysical properties of the battery and can contribute to the future safety design of large-format batteries.

Side plates have been commonly arranged around lithium-ion batteries to ensure their structural stability when combining modules, which could affect the heat transfer path once thermal runaway propagation occurs in the battery modules, owing to the good thermal conductivity of side plates. However, the effect of side plates on the propagation characteristics is not comprehensively studied. In this paper, a validated three-dimensional thermal runaway propagation model of a battery module is used to investigate the propagation behaviors under different design schemes with side plates. A parametric study is carried out to determine the energy flow distribution of battery modules with and without side plates, and to evaluate the effect of the structural and thermal boundary parameters of the side plates on the failure propagation behaviors. The results indicate that the side plates delay the initial thermal runaway trigger time, considerably affect energy flow distribution, and aggravate the heat transfer. Approximately 40.8% of effluent energy passes through the side plates, dominating the critical point for battery thermal runaway propagation. Furthermore, with side plates surrounding the module, the initial thermal runaway trigger time is delayed by 12.7%, whereas the average propagation time interval between adjacent batteries is shortened by 27.4%, compared to those without side plates. An increase in the thickness, area, and aspect ratio of the side plates can postpone the initial thermal runaway trigger time, while shortening the average propagation time interval. This work adds new knowledge in the failure propagation mechanism within battery modules with side plates, enlightening the thermal safety design of high-energy battery modules.

The estimation of remaining discharge energy (RDE) of lithium-ion batteries is the basis for the remaining driving range estimation of electric vehicles. The RDE estimation is affected by many factors, such as battery future load, battery ageing and temperature. In this study, an RDE estimation method based on the future load prediction considering battery temperature and ageing effects is proposed. First, the hidden Markov model (HMM) is implemented to predict the future load of battery. Then, the capacity test at different temperatures is conducted to determine the limited state-of-charge (SOC) in the prediction field. Third, a forgetting factor recursive least square (FFRLS) algorithm is used to identify and update the battery model parameters online to address the parameter mismatch issue caused by battery ageing and temperature fluctuation. Finally, based on the predicted current, SOC, and voltage sequences, the RDE is estimated under different operation conditions. In particular, a battery simulation driving condition is constructed using the real vehicle speed to verify the effectiveness of the proposed method in complex conditions. The results demonstrate that the accuracy and robustness of the proposed method against various operation conditions and battery ageing are satisfactory.

The decrease in capacity at low temperature limits further application of lithium-ion batteries. This paper introduces an approach to compensating the capacity loss of Li4Ti5O12 (LTO) based lithium-ion batteries at low temperature by increasing the charging cut-off voltage. The impact of this approach on battery durability is examined afterwards, and a “tipping point” voltage for degradation is observed. Cut-off voltages over the tipping point (3.2 V) cause severe degradation, while those beneath the tipping point do not. The degradation mechanisms are studied with incremental capacity (IC) curve analysis, a prognostic and mechanistic (P&M) model and Scanning Electron Microscope/X-Ray Energy Dispersive Spectrometer (SEM/EDS) tests. Analysis show that the potential of the LTO anode first drops below 1 V when the battery is overcharged to 3.2 V. The electrolyte exceeds the reduction potential and decomposes on the LTO surface, forming Solid Electrolyte Interface (SEI) film with gas generation. The anode potential then gradually rises to above 1 V as we continue to charge the battery. However, the cathode potential exceeds 4.6 V, resulting in electrolyte oxidation and active material loss in the cathode. Overall, enhancing the cut-off voltage from 2.7 V to 3.2 V is effective to enlarge the charging capacity (9.5%) of the LTO batteries at −20℃ with slight degradation.

Thermal runaway (TR) is a major safety concern in lithium-ion batteries. Model-based TR prediction is critically needed to optimize safety designs of cells. This paper presents a novel scheme for developing reliable battery TR model from kinetics analysis of cell components. First, differential scanning calorimetry (DSC) tests on the individual cell components and their mixtures are conducted to reveal the TR mechanism and characterize the exothermic reactions, of which the major six (such as the decomposition of solid electrolyte interface (SEI) film) are determined as the dominant heat sources. The kinetics parameters of each exothermic reactions are identified from the DSC tests results at variant heating rates using Kissinger’s method and nonlinear fitting method. A predictive battery TR model is established by superimposing the chemical kinetics equations of the six exothermic reactions. The model fits well with the adiabatic TR test results and the oven tests results of a 24 Ah lithium-ion battery, indicating that the model can well reflect the battery TR mechanism and be trusted to predict battery safety performance without assembling a real battery.

This paper presents a performance analysis of plug-in hybrid electric vehicles (PHEVs) considering battery preheating economy under low temperature conditions. In subzero temperature environments, PHEVs suffer a dramatic loss of all-electric driving range due to the energy and power reduction of LiFePO4 batteries, as well as severe battery degradation due to lithium ion plating. This decreases the battery life time and thus increases the operating cost of the PHEV. A quasi-static model is adopted for the simulated bus, and a battery dynamic degradation model is established based on the Arrhenius degradation theory. The PHEV performance under low-temperature conditions is evaluated considering three factors: fuel cost, electricity cost, and battery degradation cost. In addition, the economics of battery preheating powered by the engine or grid is first investigated in this paper. The charge depleting-charge sustaining energy management strategy and convective heating method are adopted. Simulation results show that the preheating strategy can reduce the PHEV operating cost by up to 22.3% in 40 Harbin driving cycles. The heating process becomes increasingly necessary as the battery price, heating efficiency, and daily recharging time of the PHEV increase as well as the environment temperature decreases.

Summary We demonstrate herein that not only internal short circuiting, but also chemical crossover, is the mechanism behind thermal runaway that can occur in lithium-ion batteries due to abuse conditions. In situ experiments showed that during thermal runaway, the cathode releases oxygen by a phase transition, and this oxygen is consumed by the lithiated anode. The released highly oxidative gas reacts with reductive LiCx with tremendous heat generation centered at 274.2°C with heat flow of 87.8 W g−1. To confirm the proposed mechanism, we froze a battery undergoing the thermal runaway process by liquid nitrogen and subjected it to detailed post-test analysis. Our results revealed the hidden thermal runaway mechanism of chemical crossover between the battery components without a severe internal short circuit. These findings provide an important insight into the rational design of automotive lithium-ion batteries as well as solid-state batteries.

Internal short circuit (ISCr) is one of the major obstacles to the improvement of the battery safety. The ISCr may lead to the battery thermal runaway and is hard to be detected in the early stage. In this work, a new ISCr detection method based on the symmetrical loop circuit topology (SLCT) is introduced. The SLCT ensures that every battery has the same priority in the circuit and every battery will contribute the same amount of short-circuit current to the ISCr once the ISCr happens. The ISCr battery could be identified by the combination of the ratio of the short-circuit currents and the sign of the short-circuit currents. The recursive least square method is adopted for the real-time application and the optimized ammeters allocation is derived from the mathematic deduction. The battery pack based on the individual DP (dual polarization) battery model is established to verify the ISCr detection method. The 1–1000 Ω s ISCr (the early stage ISCr) can be effectively detected within 1–125 s. The SLCT provides the possibility of new battery pack designs and new battery management methods. The proposed ISCr detection method shows excellent effectiveness and efficiency on the identification of the ISCr battery in the early stage.

Micro-short circuit (MSC) of a lithium-ion battery cell is a potential safety hazard for battery packs. How to identify the cell with MSC in the latent phase before a thermal runaway becomes a difficult problem to solve. We propose a diagnosis method to detect the MSC according to the remaining charging capacity (RCC) variations between cells. When the charging cell voltage curve (CCVC) of the first fully charged cell is regarded as the benchmark, the RCC of each cell can be obtained by the CCVC transformation based on the uniform CCVC hypothesis. The accuracy of the RCC estimation method is validated under constant current and constant power charging experiments. The leakage current of the MSC cell can be obtained by the increase of the RCC after each charge, and it can be converted into the MSC resistance. We set up the battery pack system model with the MSC fault in Simulink® to verify the effectiveness of the method when the battery pack is charged up to different charge cutoff voltages. Through a series of experiments with external resistors, the adaptability of the MSC diagnosis method is further validated for the aged cell, multi-stage charging, and constant power charging.

The United States and China are the world’s largest automobile markets and oil consumers, and both face a severe challenge to conserve energy and reduce tailpipe emissions. Thus, both countries urgently need to transform conventional internal combustion engines to electrified powertrains. Targeting the advanced core technologies of plug-in electric vehicles (PEVs), a joint research collaboration between China and the US, called the “Clean Vehicle Consortium” (CVC), was set up in 2010. Six years of collaboration on PEV technologies has resulted in significant progress in three technical areas. Based on CVC publications, we review herein the progress made by the CVC research efforts on three key advanced PEV technologies. This includes the development of a safe battery with an energy density of 260 W h kg−1 and a systematic method for designing safe traction battery systems. Thus, a breakthrough in high power density and efficient traction motor systems has occurred. In addition to discussing advanced electric-drive powertrains, we also discuss global energy management strategies that aim to improve PEV energy efficiency. This discussion covers scientific and comprehensive analysis methods to analyze energy systems, which include cost-benefit analyses of plug-in hybrid electric vehicles, life-cycle assessments for evaluating vehicle emissions, and PEV-ownership projections.

The Pontryagin's minimum principle is utilized in this paper to determine the best solution of component sizing and energy management strategy for a plug-in hybrid electric vehicle which is equipped with a hybrid energy storage system. The hybrid energy storage system, including batteries and supercapacitors, is an effective solution to extend battery life span and reduce the vehicle operating cost. The operating costs of different hybrid energy storage system candidates, including fuel cost, electricity cost, and battery degradation cost over 6 consecutive China bus driving cycles, are minimized by using a 2-dimensional Pontryagin's minimum principle algorithm proposed in this paper. The proposed Pontryagin's minimum principle algorithm not only determines the optimal energy management strategy, but also globally finds the optimal battery and supercapacitor sizes. It is shown that the operating cost strictly decreases with increasing battery and supercapacitor sizes. In addition, simulation results show that the operating cost is reduced by up to 28.6% when compared to a conventional hybrid powertrain without supercapacitors. Thus the effectiveness of adopting supercapacitors in plug-in hybrid electric vehicles is verified.

Electric buses face problems of short driving range, slow charging, and high cost. To improve the performance of electric buses, a novel hybrid battery system (HBS) configuration consisting of lithium iron phosphate (LFP) batteries and Li-ion batteries with a Li4Ti5O12 (LTO) material anode is proposed. The configuration and control of the HBS are first studied, and a LFP battery degradation model is built. Simulation result indicates that the HBS can help us to mitigate LFP battery degradation. Then, the HBS is optimally sized for electric buses to achieve minimum cost. The daily bus operation and charging patterns as well as LFP battery degradation are considered. The optimal HBS has 10.7% and 19.3% lower total cost than the single LTO-battery and LFP-battery configurations, and has higher range flexibility than the single LTO-battery configuration.

This paper proposes a co-estimation scheme of state of charge (SOC), state of health (SOH), and state of function (SOF) for lithium-ion batteries in electric vehicles. The co-estimation denotes that the SOC, SOH, and SOF are estimated simultaneously in real-time application. The model-based SOC estimation is fulfilled by the extended Kalman filter. The battery parameters related with the battery SOH and SOF are online identified using the recursive least square algorithm with a forgetting factor. The capacity and the maximum available output power are then estimated based on the identified parameters. The online update of the capacity and correlated parameters help improve the accuracy of the state estimation but with limited increase in the computation load, by making good use of the correlations among the states. The co-estimation scheme is validated in a real battery management system with good real-time performance and convincible estimation accuracy.