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Despite the widespread utilization of Lithium-ion batteries (LIBs), concerns regarding safety during operation persist owing to accidents and potential risks of fires and explosions. To comprehend the thermal dynamics that underlie severe LIB incidents, calorimetry tests have been prevalently employed for over three decades to examine the exothermic/endothermic behavior, reaction kinetics, and thermal interactions among LIB materials. There exists a substantial volume of calorimetry test results on various LIB electrodes, electrolytes, and other components. However, this data showcases low consistency, yielding an unreliable database that obstructs a thorough understanding of LIB thermal behavior. In this research, a comparative analysis of differential scanning calorimetry (DSC) results from materials utilized in the most commercialized LIB systems is conducted. The analysis unveils notable discrepancies in DSC data amassed by different researchers, identifies five predominant causes of data inconsistency, proposes a standardized DSC operational procedure, and generates a set of self-consistent data. Subsequently, an intrinsic safety spectrum is delineated and compared with X-ray diffraction (XRD) outcomes to elucidate the correlation between the crystal lattice structure and the thermal behavior of the material. This work aids in the formation of a comparative DSC database, utilizing the vast but inconsistent literature data. Moreover, it clarifies the linkage between the material structure and thermal behavior, facilitating data-driven thermal analysis of LIBs.

Selecting adequate ferritic stainless steel (FSS) with a high corrosion resistance and a low cost is critical for solid oxide fuel cells (SOFCs) operating at intermediate temperature. In this study, the corrosion behaviors of four commercial FSSs involving TS430, TY441, YG442, and TY445 with a Cr content ranging from 16.18 wt.% to 21.73 wt.% are investigated at 650 °C. The oxidation mass gains, microstructures of surface oxide scale, and electrical conductivities are measured. The effects of grain size as well as doped elements are estimated together with the Cr volatilization. Flaky Cr2O3 particles are formed on TS430 and TY441 dominated by the outward migration of Cr3+. In comparison, a thin and dense layer of chromia is observed on YG442 and TY445. A high Cr content and a uniformly distributed grain size are conducive to the formation of a thin and dense chromia scale on the FSS surface during the initial oxidation process. On the other hand, the addition of Nb, Ti, and Mo weakens the outward diffusion of Cr3+ and reduces the particle size of chromia. After oxidation at 650 °C for 120 h, scattered (Mn, Cr)3O4 spinel particles occur on TS430, YG442, and TY445. TY445 and YG442 exhibit a higher conductivity although all the results of area specific resistance (ASR) are less than 6 mΩ·cm2. Meanwhile, the effect of Cr volatilization is enlarged on the estimation of mass gain at 650 °C compared with even higher temperatures.

Abstract With the electrochemical performance of batteries approaching the bottleneck gradually, it is increasingly urgent to solve the safety issue. Herein, all‐in‐one strategy is ingeniously developed to design smart, safe, and simple (3S) practical pouch‐type LiNi0.8Co0.1Mn0.1O2||Graphite@SiO (NCM811||Gr@SiO) cell, taking full advantage of liquid and solid‐state electrolytes. Even under the harsh thermal abuse and high voltage condition (100 °C, 3–4.5 V), the pouch‐type 3S NCM811||Gr@SiO cell can present superior capacity retention of 84.6% after 250 cycles (based pouch cell: 47.8% after 250 cycles). More surprisingly, the designed 3S NCM811||Gr@SiO cell can efficiently improve self‐generated heat T1 by 45 °C, increase TR triggering temperature T2 by 40 °C, and decrease the TR highest T3 by 118 °C. These superior electrochemical and safety performances of practical 3S pouch‐type cells are attributed to the robust and stable anion‐induced electrode‐electrolyte interphases and local solid‐state electrolyte protection layer. All the fundamental findings break the conventional battery design guidelines and open up a new direction to develop practical high‐performance batteries.

The energy revolution requires coordination in energy consumption, supply, storage and institutional systems. Renewable energy generation technologies, along with their associated costs, are already fully equipped for large-scale promotion. However, energy storage remains a bottleneck, and solutions are needed through the use of electric vehicles, which traditionally play the role of energy consumption in power systems. To clarify the key technologies and institutions that support EVs as terminals for energy use, storage, and feedback, the CSEE JPES forum assembled renowned experts and scholars in relevant fields to deliver keynote reports and engage in discussions on topics such as vehicle-grid integration technology, advanced solid-state battery technology, high-performance electric motor technology, and institutional innovation in the industry chain. These experts also provided prospects for energy storage and utilization technologies capable of decarbonizing new power systems.

Abstract Accurate evaluation of Li-ion battery (LiB) safety conditions can reduce unexpected cell failures, facilitate battery deployment, and promote low-carbon economies. Despite the recent progress in artificial intelligence, anomaly detection methods are not customized for or validated in realistic battery settings due to the complex failure mechanisms and the lack of real-world testing frameworks with large-scale datasets. Here, we develop a realistic deep-learning framework for electric vehicle (EV) LiB anomaly detection. It features a dynamical autoencoder tailored for dynamical systems and configured by social and financial factors. We test our detection algorithm on released datasets comprising over 690,000 LiB charging snippets from 347 EVs. Our model overcomes the limitations of state-of-the-art fault detection models, including deep learning ones. Moreover, it reduces the expected direct EV battery fault and inspection costs. Our work highlights the potential of deep learning in improving LiB safety and the significance of social and financial information in designing deep learning models.

Abstract The electrochemical–mechanical degradation of ultrahigh Ni cathode for lithium‐ion batteries is a crucial aspect that limits the cycle life and safety of devices. Herein, the study reports a facile strategy involving rational design of primary grain crystallographic orientation within polycrystalline cathode, which well enhanced its electro‐mechanical strength and Li+ transfer kinetics. Ex situ and in situ experiments/simulations including cross‐sectional particle electron backscatter diffraction (EBSD), single‐particle micro‐compression, thermogravimetric analysis combined with mass spectrometry (TGA‐MS), and finite element modeling reveal that, the primary‐grain‐alignment strategy effectively mitigates the particle pulverization, lattice oxygen release thereby enhances battery cycle life and safety. Besides the preexisting doping and coating methodologies to improve the stability of Ni‐rich cathode, the primary‐grain‐alignment strategy, with no foreign elements or heterophase layers, is unprecedently proposed here. The results shed new light on the study of electrochemical–mechanical strain alleviation for electrode materials.

A compact wireless near-field hydrogen gas sensor is proposed, which detects leaking hydrogen near its source to achieve fast responses and high reliability. A semiconductor-type sensing element is implemented in the sensor, which can provide a significant response in 100 ms when stimulated by pure hydrogen. The overall response time is shortened by orders of magnitude compared to conventional sensors according to simulation results, which will be within 200 ms, compared with over 25 s for spatial concentration sensors under the worst conditions. Over 1 year maintenance intervals are enabled by wireless design based on the Bluetooth low energy protocol. The average energy consumption during a single alarm process is 153 μJ/s. The whole sensor is integrated on a 20 × 26 mm circuit board for compact use.

The venting event of thermal runaway has attracted public attention due to safety issues aroused by frequent fire accidents of new energy vehicles. However, the quantitative description of venting behavior is incomplete for tests in a sealed chamber due to the initial violent injection. In this study, nine types of batteries covering 28 cases in total were employed to investigate the influence of energy density, ambient temperature, pressure, and SOC on the venting behavior, characterized by normalized gas amount; maximum gas releasing rate; and venting durations t50, t90, t95, and t99. Then, a ‘two-point’ fitting method was proposed to modify outcomes concerning real-time gas amounts. The results show that at 100% SOC, the normalized gas amount ranges within 0.075–0.105 mol/Ah for NCM batteries and 0.025–0.035 mol/L for LFP batteries, while the maximum gas releasing rate presents a strongly positive correlation with the capacity of NCM batteries (0.04–0.31 mol/s) and a slight increase for LFP batteries (0.02–0.06 mol/s). Eventually, the three venting patterns were summarized and advanced according to the energy density and SOC of the targeted battery. This research can provide a reference for risk evaluations of the venting process and safety design for structure and pressure relief in battery systems.

Further applications of electric vehicles (EVs) and energy storage stations are limited because of the thermal sensitivity, volatility, and poor durability of lithium-ion batteries (LIBs), especially given the urgent requirements for all-climate utilization and fast charging. This study comprehensively reviews the thermal characteristics and management of LIBs in an all-temperature area based on the performance, mechanism, and thermal management strategy levels. At the performance level, the external features of the batteries were analyzed and compared in cold and hot environments. At the mechanism level, the heat generation principles and thermal features of LIBs under different temperature conditions were summarized from the perspectives of thermal and electrothermal mechanisms. At the strategy level, to maintain the temperature/thermal consistency and prevent poor subzero temperature performance and local/global overheating, conventional and novel battery thermal management systems (BTMSs) are discussed from the perspective of temperature control, thermal consistency, and power cost. Moreover, future countermeasures to enhance the performance of all-climate areas at the material, cell, and system levels are discussed. This study provides insights and methodologies to guarantee the performance and safety of LIBs used in EVs and energy storage stations.

The performance of alkaline water electrolysis (AWE) at high current densities is limited by gas bubble generation on the surface of electrodes, which covers active sites and blocks mass transfer, resulting in lower AWE efficiency. Here, we utilize electro-etching to construct Ni electrodes with hydrophilic and aerophobic surfaces to improve the efficiency of AWE. Ni atoms on the Ni surface can be exfoliated orderly along the crystal planes by electro-etching, forming micro-nano-scale rough surfaces with multiple crystal planes exposed. The 3D-ordered surface structures increase the exposure of active sites and promote the removal of bubbles on the surface of the electrode during the AWE process. In addition, experimental results from high-speed camera reveal that rapidly released bubbles can improve the local circulation of electrolyte. Lastly, the accelerated durability test based on practical working condition demonstrates that the 3D-ordered surface structures are robust and durable during the AWE process.

Abstract High‐energy‐density lithium metal batteries (LMBs) are widely accepted as promising next‐generation energy storage systems. However, the safety features of practical LMBs are rarely explored quantitatively. Herein, the thermal runaway behaviors of a 3.26 Ah (343 Wh kg−1) Li | LiNi0.5Co0.2Mn0.3O2 pouch cell in the whole life cycle are quantitatively investigated by extended volume‐accelerating rate calorimetry and differential scanning calorimetry. By thermal failure analyses on pristine cell with fresh Li metal, activated cell with once plated dendrites, and 20‐cycled cell with large quantities of dendrites and dead Li, dendrite‐accelerated thermal runaway mechanisms including reaction sequence and heat release contribution are reached. Suppressing dendrite growth and reducing the reactivity between Li metal anode and electrolyte at high temperature are effective strategies to enhance the safety performance of LMBs. These findings can largely enhance the understanding on the thermal runaway behaviors of Li metal pouch cells in practical working conditions.

The internal negative electrode potential in lithium-ion batteries (LIBs) is intricately linked to the lithium-ion intercalation and plating reactions occurring within the cell. With the expansion of cell sizes, the internal negative electrode potential distribution gradually becomes inconsistent. However, the existing negative electrode potential estimation models and fast charging strategies have not yet considered the impact of consistency, and the model estimation accuracy will be greatly influenced by different temperatures and charging rates. This study proposes an online lithium-free fast charging equivalent circuit model (OLFEM) for estimating the negative electrode potential terminal voltage and developing fast charging strategies of long-dimensional LIBs in real vehicles. This study employs distributed reference electrodes integrated into long-dimensional LIBs and compares the negative electrode potential measured in the vicinity of both the negative and positive tabs. Subsequently, based on the lowest negative electrode potential point, model parameters were obtained at different temperatures and charging rates. This model is further verified under different operating conditions. Finally, a fast-charging strategy without lithium plating is developed in real-time based on the negative electrode potential estimated by the model. The results demonstrate that long-dimensional cells exhibit a lower negative electrode potential on the positive tab side. Across various temperatures and charging rates, the calibrated model achieves a negative electrode potential estimated error within 25 mV, and the estimation error for terminal voltage is within 5 mV. The proposed fast-charging method prevents lithium plating and charges the cell up to 96.8% within an hour. After 100 cycles, the cell experiences a capacity degradation of less than 2%, and the disassembly results indicate that no lithium precipitation has occurred. The methods outlined in this study provide valuable insights for online fast charging of large-dimensional batteries without lithium plating.

Renewable energy power generation systems such as photovoltaic and wind power have characteristics of intermittency and volatility, which can cause disturbances to the grid frequency. The battery system of electric vehicles (EVs) is a mobile energy storage system that can participate in bidirectional interaction with the power grid and support the frequency stability of the grid. Lithium iron phosphate (LiFePO4) battery systems, with their advantages of high safety and long cycle life, are widely used in EVs and participate in frequency regulation (FR) services. Accurate assessment of the state of charge (SOC) and remaining available energy (RAE) status in LiFePO4 batteries is crucial in formulating control strategies for battery systems. However, establishing an accurate voltage model for LiFePO4 batteries is challenging due to the hysteresis of open circuit voltage and internal temperature changes, making it difficult to accurately assess their SOC and RAE. To accurately evaluate the SOC and RAE of LiFePO4 batteries in dynamic FR working conditions, a thermal-electric-hysteresis coupled voltage model is built. Based on this model, closed-loop optimal SOC estimation is achieved using the extended Kalman filter algorithm to correct the initial value of SOC calculated by ampere-hour integration. Further, RAE is accurately estimated using a method based on future voltage prediction. The research results demonstrate that the thermal-electric-hysteresis coupling model exhibits high accuracy in simulating terminal voltage under a 48 h dynamic FR working condition, with a root mean square error (RMSE) of only 18.7 mV. The proposed state estimation strategy can accurately assess the state of LiFePO4 batteries in dynamic FR working conditions, with an RMSE of 1.73% for SOC estimation and 2.13% for RAE estimation. This research has the potential to be applied in battery management systems to achieve an accurate assessment of battery state and provide support for the efficient and reliable operation of battery systems.

During the manufacturing process of the lithium-ion battery, metal foreign matter is likely to be mixed into the battery, which seriously influences the safety performance of the battery. In order to reduce the outflow of such foreign matter defect cells, the production line universally adopted the K-value test process. In the traditional K-value test, the detection threshold is determined empirically, which has poor dynamic characteristics and probably leads to missing or false detection. Based on comparing the screening effect of different machine learning algorithms for the production data of lithium-ion cells, this paper proposes a K-value dynamic screening algorithm for the cell production line based on the local outlier factor algorithm. The analysis results indicate that the proposed method can adaptively adjust the detection threshold. Furthermore, we validated its effectiveness through the metal foreign matter implantation experiment conducted in the pilot manufacturing line. Experiment results show that the proposed method’s detection rate is improved significantly. The increase in the detection rate of foreign matter defects is beneficial to improving battery quality and safety.

Battery life management is critical for plug-in hybrid electric vehicles (PHEVs) to prevent dangerous situations such as overcharging and over-discharging, which could cause thermal runaway. PHEVs have more complex operating conditions than EVs due to their dual energy sources. Therefore, the SOH estimation for PHEV vehicles needs to consider the specific operating characteristics of the PHEV and make calibrations accordingly. Firstly, we estimated the initial SOH by combining data-driven and empirical models. The data-driven method used was the incremental state of charge (SOC)-capacity method, and the empirical model was the Arrhenius model. This method can obtain the battery degradation trend and predict the SOH well in realistic applications. Then, according to the multiple characteristics of PHEV, we conducted a correlation analysis and selected the UF as the calibration factor because the UF has the highest correlation with SOH. Finally, we calibrated the parameters of the Arrhenius model using the UF in a fuzzy logic way, so that the calibrated fitting degradation trends could be closer to the true SOH. The proposed calibration method was verified by a PHEV dataset that included 11 vehicles. The experiment results show that the root mean square error (RMSE) of the SOH fitting after UF calibration can be decreased by 0.2–14% and that the coefficient of determination (R2) for the calibrated fitting trends can be improved by 0.5–32%. This provides more reliable guidance for the safe management and operation of PHEV batteries.

The lithium iron phosphate (LiFePO4) blade battery is a long, rectangular-shaped cell that can be directly integrated into battery pack systems. It enhances volumetric power density, significantly reduces costs, and is widely utilized in electric vehicles. However, the flat open circuit voltage and significant polarization differences under wide operational temperatures are challenging for accurate voltage modeling of battery management systems (BMSs). In particular, inaccurate state of charge (SOC) estimation may cause overcharging and over-discharging risks. To accurately perceive the SOC of LiFePO4 blade batteries, a SOC estimation method based on the backpropagation neural network-extended Kalman filter (BPNN-EKF) algorithm is proposed. BPNN is a neural network model that utilizes the backpropagation algorithm to update model parameters, while EKF is an optimal estimation algorithm. Firstly, dynamic working condition tests, including the New European Driving Cycle (NEDC) and high-speed working (HSW) condition tests, are conducted under a wide temperature range (−25–43 °C). HSW conditions refer to a simulated operating condition that mimics the driving of an electric vehicle on a highway. The minimum voltage of the battery system is used as the output for training the BPNN model. We derive the Kalman gain by combining the BPNN output voltage. Additionally, the EKF algorithm is employed to correct the SOC value using voltage error information. Concerning long SOC calculation intervals, capacity errors, initial SOC errors, and current and voltage sampling errors, the maximum SOC estimation RMSE is 3.98% at −20 °C NEDC, 3.62% at 10 °C NEDC, and 1.68% at 35 °C HSW. The proposed algorithm can be applied to different temperatures and operations, demonstrating high robustness. This BPNN-EKF algorithm has the potential to be embedded in electric vehicle BMS systems for practical applications.

Hydrogen as an energy carrier is critical for building a zero-carbon emission society. Solid oxide electrolysis cell (SOEC) is a feasible technology for hydrogen production with a high efficiency. Currently, the durability of SOEC systems still needs to be improved and technical issues need to be overcome. Reducing the working temperature is helpful for the lifetime. A good cell design to avoid delamination is also very important. In this study, the performance of a metal-supported intermediate-temperature SOEC is estimated using gadolinium doped ceria Gd0.1Ce0.9O2-δ (GDC) as the main electrolyte. First, a mathematical model is setup for the metal-supported SOEC. The effects of the porosity and tortuosity of the electrodes are analyzed. Subsequently, the influences of the working temperature, pressure, and steam concentration are estimated. Finally, the partial oxygen pressure inside the multi-layer electrolyte is determined and the risk of delamination is discussed. The results indicate that increasing the operation temperature can decrease the activation, concentration, and ohmic overpotentials simultaneously while increasing the pressure also can enhance the performance. Compared with the conventional design of Ceres Power, the new design using 10Sc1CeSZ as the barrier layer can increase the partial oxygen pressure of the GDC layer close to the cathode such that decomposition of GDC is avoided. Meanwhile, the partial oxygen pressure inside the multi-layer electrolyte close to the anode declines and the risk of delamination is reduced. Hence, the new design of the SOEC is beneficial for the durability of metal-supported SOEC.

The role of the lithiated graphite anode in battery thermal runaway failure remains under intense investigation. In this work, with multiple in situ synchrotron X-ray characterizations, the phase evolution, gas release, and lithium leaching of lithiated graphite anode are illustrated in detail.

This paper proposes a framework for generating a battery test profile that accounts for the complex operating conditions of electric vehicles, which is essential for ensuring the durability and safety of the battery system used in these vehicles. Additionally, such a test profile could potentially accelerate the development of electric vehicles. To achieve this objective, the study utilizes a simplified longitudinal dynamics model that incorporates various factors such as the drivetrain efficiency, battery system energy conversion efficiency, and regenerative braking efficiency. The battery test profile is based on the China light-duty vehicle test cycle-passenger car (CLTC-P) and is validated through testing on an electric vehicle with a chassis dynamometer. The results indicate a high degree of consistency between the generated and measured profiles, confirming the efficacy of the simplified longitudinal dynamics model.

Lithium batteries are being utilized more widely, increasing the focus on their thermal safety, which is primarily brought on by their thermal runaway. This paper’s focus is the energy storage power station’s 50 Ah lithium iron phosphate battery. An in situ eruption study was conducted in an inert environment, while a thermal runaway experiment was conducted utilizing sealed pressure containers and an external heating triggering mechanism. Both the amount of gas release and the battery’s maximum temperature were discovered. Using gas chromatography, the gas emission from the battery was examined. Its principal constituents included CO, H2, CO2, CH4, C2H4, and so on. Moreover, the experiment discovered a second eruption of lithium iron phosphate, and the stage of its eruption was separated by the pressure signal of the sealed experimental chamber, giving a theoretical foundation and technological backing for the thermal catastrophe safety of lithium batteries.

The accuracy of capacity estimation is of great importance to the safe, efficient, and reliable operation of battery systems. In recent years, data-driven methods have emerged as promising alternatives to capacity estimation due to higher estimation accuracy. Despite significant progress, data-driven methods are mainly developed by experimental data under well-controlled charge–discharge processes, which are seldom available for practical battery health monitoring under realistic conditions due to uncertainties in environmental and operational conditions. In this paper, a novel method to estimate the capacity of large-format LiFePO4 batteries based on real data from electric vehicles is proposed. A comprehensive dataset consisting of 85 vehicles that has been running for around one year under diverse nominal conditions derived from a cloud platform is generated. A classification and aggregation capacity prediction method is developed, combining a battery aging experiment with big data analysis on cloud data. Based on degradation mechanisms, IC curve features are extracted, and a linear regression model is established to realize high-precision estimation for slow-charging data with constant-current charging. The selected features are highly correlated with capacity (Pearson correlation coefficient < 0.85 for all vehicles), and the MSE of the capacity estimation results is less than 1 Ah. On the basis of protocol analysis and mechanism studies, a feature set including internal resistance, temperature, and statistical characteristics of the voltage curve is constructed, and a neural network (NN) model is established for multi-stage variable-current fast-charging data. Finally, the above two models are integrated to achieve capacity prediction under complex and changeable realistic working conditions, and the relative error of the capacity estimation method is less than 0.8%. An aging experiment using the battery, which is the same as those equipped in the vehicles in the dataset, is carried out to verify the methods. To the best of the authors’ knowledge, our study is the first to verify a capacity estimation model derived from field data using an aging experiment of the same type of battery.

The widespread use of electric vehicles (EVs) is challenged by the applicability of batteries at low temperatures. To improve the user experience on EVs, thermal management system is designed to heat batteries before driving in cold circumstance. Currently, the offered heating system comes at a cost, insufficient heating rates. This study investigates heating performance on batteries with driving circuits of EVs, and proposed a triple-module separated invert (TMSI) mode to rapidly heat the battery pack, with the batteries divided into three groups and connected to the bridges of inverter separately. The TMSI mode achieves a temperature rise rate of 8.6 °C/min on batteries through the pulse currents generated by driving circuits during EV parking. Besides, the battery pack can maintain an appropriate temperature during EV driving with adjustable heating power. This work provides a promising method to rapidly heat the batteries at ignorable cost, and enhance the acceptance of EVs in areas with cold climates.

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.

Electrification of heavy duty vehicles (HDVs) is critical to realization of the target of carbon neutralization in the future. For most HDVs, the influence of road slope on vehicle power usually cannot be ignored due to significant road slope variation during long driving mileages. In order to design the powertrain system for electrified HDVs effectively, it is necessary to construct representative driving cycles with road slope information. There are two difficulties for this task. (1) Road slope measuring devices are usually costly. A cheaper yet effective method for measuring road slope needs to be developed. (2) A 3D (three dimension) Markov chain method is usually utilized for constructing cycles with velocity and road slope. This method is complex and time consuming, and needs to be improved. In this paper, a 2D (two dimension) Markov chain method for addressing these issues is proposed. A road slope observation is designed based on normal GPS (Global Positioning System) signals and a high order Butterworth filter. The effectiveness of the method is validated. Driving velocity and road slope are collected and observed for the area between Beijing and Zhangjiakou in northern China. Representative cycles with road slope are constructed using a 2D Markov chain method and a matching algorithm based on average speed. With the introduced technology, three representative driving cycles with road slope for urban, suburban and highway routes are designed. Statistic results on vehicle power show that, the representative driving cycles are effective with relative errors less than 4% compared to the real driving conditions. These driving cycles will be utilized in designing electric HDVs, such as hydrogen fuel cell vehicles in the future.

In this paper, a novel method to actively damp the powertrain vibration by utilizing a flywheel integrated starter generator (FISG) is proposed and validated on the testbench. A more light-weighted, economical, and compact hybrid powertrain devoid of clutches or dampers is built by replacing its flywheel with the ISG rotor. Motor torque having the opposite phase of the engine torque is applied to reduce the vibration. As opposed to the passive methods, which usually make use of the inertial and/or the damping properties of the components to absorb the vibration, this active approach is defined as active damping. Firstly, active damping is proved to be theoretically feasible by simulation based on an engine torque observer. To get the practical-application-oriented motor torque waveform, the observer torque is then processed by considering some realistic factors, such as the motor's response delay and the torque demand limit at MCU (Motor Control Unit), and is finally simplified as the rectangular waveform by off-line simulation. Secondly, an algorithm operating on the original resolver aimed at calculating the real-time crank position is proposed and programmed directly in the MCU. Finally, active damping is realized online utilizing a feedforward method by programming the crank-torque table in MCU. Experiments in both stable and transient conditions are conducted. Results show that active damping can attenuate the vibration effectively, especially over low speed and load ranges. The speed fluctuation range and the cyclic squared angular acceleration can be reduced by 79.7% and 89.7% respectively at 700 rpm when cranking. The crank vibration in transient conditions, including dragging and free deceleration, can also be suppressed, showing potential to realize fast and quiet engine start and halt.

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.

Lithium-ion batteries (LIBs) are widely used in electric vehicles (EV) and energy storage stations (ESS). However, combustion and explosion accidents during the thermal runaway (TR) process limit its further applications. Therefore, it is necessary to investigate the uncontrolled TR exothermic reaction for safe battery system design. In this study, different LIBs are tested by lateral heating in a closed experimental chamber filled with nitrogen. Moreover, the relevant thermal characteristic parameters, gas composition, and deflagration limit during the battery TR process are calculated and compared. Results indicate that the TR behavior of NCM batteries is more severe than that of LFP batteries, and the TR reactions becomes more severe with the increase of energy density. Under the inert atmosphere of nitrogen, the primarily generated gases are H2, CO, CO2, and hydrocarbons. The TR gas deflagration limits and characteristic parameter calculations of different cathode materials are refined and summarized, guiding safe battery design and battery selection for power systems.

Lithium plating, induced by fast charging and low-temperature charging, is one of the reasons for capacity fading and causes safety problems for lithium-ion batteries. Hence, reliable and effective non-destructive detection methods for lithium plating are needed. In this research, electrochemical impedance and internal resistance for batteries are measured during the rest period after charging. The results for lithium plating batteries and normal batteries are compared and analyzed. Lithium plating detection is realized with multiple indicators extracted from electrochemical impedance and internal resistance results. The effectiveness of the proposed detection methods is verified by the experiments conducted with commercial large-capacity batteries. The proposed methods have further potential to be used in battery management systems to realize online detection of lithium plating and improve the safety of battery systems.

In the field of state estimation for the lithium-ion battery (LIB), model-based methods (white box) have been developed to explain battery mechanism and data-driven methods (black box) have been designed to learn battery statistics. Both white box methods and black box methods have drawn much attention recently. As the combination of white box and black box, physics-informed machine learning has been investigated by embedding physic laws. For LIB state estimation, this work proposes a fractional-order recurrent neural network (FORNN) encoded with physics-informed battery knowledge. Three aspects of FORNN can be improved by learning certain physics-informed knowledge. Firstly, the fractional-order state feedback is achieved by introducing a fractional-order derivative in a forward propagation process. Secondly, the fractional-order constraint is constructed by a voltage partial derivative equation (PDE) deduced from the battery fractional-order model (FOM). Thirdly, both the fractional-order gradient descent (FOGD) and fractional-order gradient descent with momentum (FOGDm) methods are proposed by introducing a fractional-order gradient in the backpropagation process. For the proposed FORNN, the sensitivity of the added fractional-order parameters are analyzed by experiments under the federal urban driving schedule (FUDS) operation conditions. The experiment results demonstrate that a certain range of every fractional-order parameter can achieve better convergence speed and higher estimation accuracy. On the basis of the sensitivity analysis, the fractional-order parameter tuning rules have been concluded and listed in the discussion part to provide useful references to the parameter tuning of the proposed algorithm.

The state estimation of lithium-ion battery is the basis of an intelligent battery management system; therefore, both model-based and data-driven methods have been designed and developed for state estimation. Rather than using complex partial differential equations and the complicated parameter tuning of a model-based method, a machine learning algorithm provides a new paradigm and has been increasingly applied to cloud big-data platforms. Although promising, it is now recognized that big data for machine learning may not be consistent in terms of data quality with reliable labels. Moreover, many algorithms are still applied as a black box that may not learn battery inner information well. To enhance the algorithm generalization in realistic situations, this paper presents a fractional-order physics-informed recurrent neural network (PIRNN) for state estimation. The fractional-order characteristics from battery mechanism are embedded into the proposed algorithm by introducing fractional-order gradients in backpropagation process and fractional-order constraints into the convergence loss function. With encoded battery knowledge, the proposed fractional-order PIRNN would accelerate the convergence speed in training process and achieve improved prediction accuracies. Experiments of four cells under federal urban driving schedule operation conditions and different temperatures are conducted to illustrate the estimation effects of the proposed fractional-order PIRNN. Compared to the integer-order gradient descent method, the fractional-order gradient descent method proposed in this work can optimize network convergence and obtains regression coefficient larger than 0.995. Moreover, the experimental results indicate that the proposed algorithm can achieve 2.5% estimation accuracy with the encoding fractional-order knowledge of lithium-ion batteries.

Lithium-ion batteries on electric vehicles have been increasingly deployed for the enhancement of grid reliability and integration of renewable energy, while users are concerned about extra battery degradation caused by vehicle-to-grid (V2G) operations. This paper details a multi-year cycling study of commercial 24 Ah pouch batteries with Li(NiMnCo)O2 (NCM) cathode, varying the average state of charge (SOC), depth of discharge (DOD), and charging rate by 33 groups of experiment matrix. Based on the reduced freedom voltage parameter reconstruction (RF-VPR), a more efficient non-intrusive diagnosis is combined with incremental capacity (IC) analysis to evaluate the aging mechanisms including loss of lithium-ion inventory and loss of active material on the cathode and anode. By analyzing the evolution of indicator parameters and the cumulative degradation function (CDF) of the battery capacity, a non-linear degradation model with calendar and cyclic aging is established to evaluate the battery aging cost under different unmanaged charging (V0G) and V2G scenarios. The result shows that, although the extra energy throughput would cause cyclic degradation, discharging from SOC 90 to 65% by V2G will surprisingly alleviate the battery decaying by 0.95% compared to the EV charged within 90–100% SOC, due to the improvement of calendar life. By optimal charging strategies, the connection to the smart grid can potentially extend the EV battery life beyond the scenarios without V2G.

The LiFePO4 (LFP) battery tends to underperform in low temperature: the available energy drops, while the state of charge (SOC) and residual available energy (RAE) estimation error increase dramatically compared to the result under room temperature, which causes mileage anxiety for drivers. This paper introduces an artificial intelligence-based electrical–thermal coupling battery model, presents an application-oriented procedure to estimate SOC and RAE for a reliable and effective battery management system, and puts forward a model-based strategy to control the battery thermal state in low temperature. Firstly, an LFP battery electrical model based on artificial intelligence is proposed to estimate the terminal voltage, and a thermal resistance model with an EKF estimation algorithm is established to assess the temperature distribution in the battery pack. Then, the electrical and thermal models are coupled, a closed-loop EKF algorithm is employed to estimate the battery SOC, and a fusion method is discussed. The coupled model is simulated under a given protocol and RAE can be obtained. Finally, based on the electrical–thermal coupling model and RAE calculation algorithm, a preheating method and constant power condition-based RAE estimation are discussed, and the thermal management strategy of the battery system under low temperature is formed. Results show that the estimation error of SOC can be controlled within 2% and RAE can be controlled within 4%, respectively. The preheating strategy at low temperature and low SOC can significantly improve the energy output of the battery pack system.

The physics-based fractional-order model (FOM) for lithium-ion batteries has shown good application prospects due to its mechanisms and simplicity. To adapt the model to higher-level applications, this paper proposes an improved FOM considering electrolyte-phase diffusion (FOMe) and then proposes a complete method for parameter identification based on three characteristic SOC intervals: the positive solid phase, negative solid phase, and electrolyte phase. The method mainly determines the above three characteristic intervals and identifies four thermodynamic parameters and five dynamic parameters. Furthermore, the paper describes a framework, which first verifies the model and parameter identification method separately based on pseudo two-dimensional model simulations, and secondly verifies FOMe and its parameters as a whole based on the experiments. The results, which are based on simulations and actual Li0.8Co0.1Mn0.1O2 lithium-ion batteries under multiple typical operating profiles and comparisons with other parameter identification methods, show that the proposed model and parameter identification method is highly accurate and efficient.

A solid oxide fuel cell is a high-efficiency power device in hydrogen energy utilization. The durability and dynamic performance of metal-supported solid oxide fuel cells (MS-SOFCs) are superior to those of electrolyte- or electrode-supported cells, with many potential applications. Gadolinium-doped cerium (GDC) has a high oxygen ionic conductivity, making it suitable to act as the electrolyte in MS-SOFCs operating at 500–650 °C. However, the low-temperature sintering of GDC is difficult for MS-SOFCs. In this study, the factors affecting the low-temperature densification of GDC are analyzed based on an orthogonal experimental method. The shrinking rates of 16 experiments are determined. The effects of the particle diameter, pressure of the uniaxial press machine, sintering temperature, and fractions of aid and binder are estimated. The results of a range analysis indicate that the content of sintering aid has the greatest impact on the low-temperature densification of GDC, followed by the powder diameter and the uniaxial pressure. A maximum shrinking rate of 46.99% is achieved with a temperature of 1050 °C.

Summary: Lithium-ion batteries are applied in electric vehicles to mitigate climate change. However, their practical applications are impeded by poor safety performance owing mainly to the cell eruption gas (CEG) fire triangle. Here, we report quantitatively the three fire boundaries corresponding to the CEG fire triangle of four types of mainstream cells with the state of charge (SOC) values ranging from 0% to 143% based on 29 thermal runaway tests conducted in an inert atmosphere in open literature. Controlling the SOC and/or selecting a reasonable cell type can alter the minimum CEG and oxygen concentrations required for ignition, thereby changing the probability of a battery fire. The ignition temperature varies greatly according to the type of ignition source type. Temperature and ignition source type play a leading role in the ignition mode. Breaking any fire boundary will stop the ignition of CEG, thus significantly improving the battery safety performance.

Wireless charging technology adapts to the electrified, lightweight, and intelligent trends of electric vehicle development. However, energy efficiency of wireless charging is still lower than conductive charging, and it is important for the wireless charging system to achieve global efficiency optimization in a constant current-constant voltage (CC-CV) charging operation. This paper proposes a novel system configuration, which adds an active power source into the receiver of the wireless charging system. It is proved that the efficiency can be increased significantly with amplitude and phase of the active power source be properly set. A novel current decoupling algorithm is used to analyze the efficiency of the new configuration, and a simulation model is established to validate the theoretical result and to explain the mechanism of efficiency improvement. The simulation shows that the active power source is able to change the power distribution and the current balance, and as a result, the efficiency increases in part of the operating region. The proposed configuration is realized in the experiment and then the efficiency improvement is demonstrated. The experimental result shows that the efficiency increases from 84.9% to 95.7% in a low coupling and slightly detuning working condition.

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.

In the commercialization process of wireless electric vehicle charging (WEVC), it is essential to ensure the interoperability between diverse WEVC systems due to the wide application of various coil configurations and compensation topologies. This paper proposes a novel electrical interoperability evaluation method based on impedance indices and corresponding feasible space in the complex plane. Firstly, the electromagnetic description of the coil system is introduced to reveal the energy flow process of WEVC system. Further, two key impedance indices and their feasible space are derived and verified. Interoperability evaluation results show that the reference devices in Chinese WEVC standard GB/T 38775.6 and GB/T 38775.7 are able to achieve the requirements of power capability. Moreover, it is necessary to reduce the duty cycle of rectifier when the battery voltage rises so as to narrow down the variation of load resistance and avoid dangerous working conditions. The proposed method can effectively evaluate the electrical interoperability of WEVC systems from different manufacturers under different power or distance levels before conducting experiments.

A great many EVs in cold areas suffer from range attenuation in winter, which causes driver anxiety concerning the driving range, representing a hot topic. Many researchers have analyzed the reasons for range attenuation but the coupling mechanism of the battery as well as the vehicle and driving conditions have not been clearly estimated. To quantitatively investigate the driving range attenuation of electric vehicles (EVs) during winter, an EV model mainly integrated with a passenger-cabin thermal model, battery model, and vehicle dynamic model was constructed and simulated based on the mass-produced Wuling HongGuang Mini EV. Real vehicle dynamic driving data was used to validate the model. Based on NEDC driving conditions, the driving range calculation formula and energy flow diagram analysis method were used. The reason for attenuation was evaluated quantitatively. Results show that battery energy loss and breaking recovery energy loss contribute nearly half of the range attenuation, which may be alleviated by battery preheating. Suggestions for extending driving range are proposed based on the research.

Thermal runaway is one of the key failure reasons for the lithium-ion batteries. The potential of thermal runaway in applications increases when the industry starts to use high energy LiNixCoyMnzO2 cathode. The thermal runaway mechanism is still unclear, because the side reactions are complex. Heat generation during thermal runaway can be caused by the decomposition of individual cell components, or by interactive reactions between multiple components. This paper tries to comb the heat sources during thermal runaway using a novel method named the “Time Sequence Map” (TSM). The TSM tracks the heat sources according to the notion of thermodynamic systems. The thermodynamic system means a combination of materials that stay and react together, and generate heat independently without interruptions from other thermodynamic systems. With the help of the defined thermodynamic systems, researchers will be rescued from being trapped in the complex reactions, and the heat sources during thermal runaway can be clearly explained from bottom up. The thermal runaway results for two battery samples demonstrate the validity of the TSM. The TSM shows the heat sources including that: (1) fire, (2) internal short circuit, (3) oxidation-reduction reaction between the cathode and anode, etc. The contributions for the heat sources to the thermal runaway are further discussed. Conclusions come to: (1) the major heat source is the oxidation-reduction reaction; (2) the fire releases lots of heat, but most of the heat is not to heat the cell itself; (3) the internal short circuit is critical to trigger the oxidation-reduction reaction; (4) the internal short circuit is not the major heat source that heat the cell to 800°C or higher; (5) the oxidation-reduction reaction is triggered when the temperature reaches a critical temperature. The TSM helps depict the frontiers in the researches of battery thermal runaway. It suggests that we focus on: (1) the relationship between internal short circuit and thermal runaway; (2) the mechanism of the oxidation-reduction reaction between the cathode and anode; (3) the detailed reaction mechanisms for a specific thermodynamic system within the cell.

Electrochemical impedance spectroscopy (EIS) is a powerful tool for investigating electrochemical systems, such as lithium-ion batteries or fuel cells, given its high frequency resolution. The distribution of relaxation times (DRT) method offers a model-free approach for a deeper understanding of EIS data. However, in lithium-ion batteries, the differential capacity caused by diffusion processes is non-negligible and cannot be decomposed by the DRT method, which limits the applicability of the DRT method to lithium-ion batteries. In this study, a joint estimation method with Tikhonov regularization is proposed to estimate the differential capacity and the DRT simultaneously. Moreover, the equivalence of the differential capacity and the incremental capacity is proven. Different types of commercial lithium-ion batteries are tested to validate the joint estimation method and to verify the equivalence. The differential capacity is shown to be a promising approach to the evaluation of the state-of-health (SOH) of lithium-ion batteries based on its equivalence with the incremental capacity.

China and the US have become the world’s largest plug-in hybrid electric vehicle (PHEV) markets. Powertrain architecture is the framework of PHEV technology which represents its technical route. The research on the market development and technical route of Chinese and American PHEV is helpful to grasp the internal law of the global PHEV market and technology situation, and thus is significant to lay out a development strategy and technical route but has not been sufficiently studied. Therefore, an evaluation method of three dimensions combining market sales, powertrain architectures and performance indexes was proposed for comparative analysis, and PHEV mainstream architectures were put forward. Besides, qualitative evaluation levels from nine dimensions were built for architecture analysis, and fuel consumption to curb weight (FC2CW) as an indicator was introduced for economy evaluation. Some conclusions can be drawn: (a) The most mainstream architecture in sales volume is four-wheel drive (4WD) Bridge, and that in models’ amount is P2 in China, while those respectively are PS and P2 in the US. This reflects that a difference exists between the choice of the consumers and that of the automakers, and another difference also exists between the two countries. (b) With the phasing down of subsidies, the single-motor parallel architecture may become the first choice of China’s next technical route, while the 4WD Bridge will still be the main architecture for sports utility vehicles (SUVs) or sports car. (c) Among the models of the top five sales, the types and sales of SUVs in China are significantly more, however, the fuel economy rankings of theirs in the US are relatively better. (d) It is difficult to distinguish which architecture has the absolute best economy, but the fuel economy of the series type in the two markets is not very good.

This paper proposes a total cost of ownership (TCO) model for battery sizing of plug-in hybrid electric vehicles (PHEVs). The proposed systematic TCO model innovatively integrates the Beijing driving database and optimal PHEV energy management strategies developed earlier. The TCO, including battery, fuel, electricity, and salvage costs, is calculated in yearly cash flows. The salvage cost, based on battery degradation model, is proposed for the first time. The results show that the optimal battery size for PHEVs in Beijing is 6–8 kWh. Several additional scenarios are also analyzed: (1) 10% increase in battery price or discount rate leads to an optimal battery size of 6 kWh, and 10% increase in fuel price shifts the optimal battery size to 8 kWh; (2) the longer and more dispersive daily range distribution in the U.S. increases the optimal battery size to 14 kWh; (3) the subsidy in China results in an optimal battery size of 13 kWh, while that in the U.S. results in 17 kWh, and a fuel savings rate based subsidy policy is innovatively proposed; (4) the optimal battery size with Li4Ti5O12 batteries is 2 kWh, but the TCO of Li4Ti5O12 batteries is higher than that of LiFePO4 batteries.

The lithium titanium oxide (LTO) anode is widely accepted as one of the best anodes for the future lithium ion batteries in electric vehicles (EVs), especially since its cycle life is very long. In this paper, three different commercial LTO cells from different manufacturers were studied in accelerated cycle life tests and their capacity fades were compared. The result indicates that under 55 °C, the LTO battery still shows a high capacity fade rate. The battery aging processes of all the commercial LTO cells clearly include two stages. Using the incremental capacity (IC) analysis, it could be judged that in the first stage, the battery capacity decreases mainly due to the loss of anode material and the degradation rate is lower. In the second stage, the battery capacity decreases much faster, mainly due to the degradation of the cathode material. The result is important for the state of health (SOH) estimation and remaining useful life (RUL) prediction of battery management system (BMS) for LTO batteries in EVs.

Incremental capacity analysis (ICA) has been used pervasively to characterize the degradation mechanisms of the lithium-ion batteries, and several online state-of-health estimation algorithms are built based on ICA. However, the stairs and the noises in the discrete sampled voltage data obstruct the calculation of the capacity differentiation over voltage (dQ/dV), therefore we need methods to fit the sampled voltage first. In this paper, the support vector regression (SVR) algorithm is used to smooth the sampled voltage curve using Gaussian kernels. A parametric study has been conducted to show how to enhance the performances of the SVR algorithm, including (1) speeding up the algorithm by downsampling; (2) avoiding overfitting and under-fitting using proper standard deviation σ in the Gaussian kernel; (3) making precise capture of the characteristic peaks. A novel method using linear approximation has been proposed to help judge the accuracy of the SVR algorithm in tracking the ICA peaks. And advanced SVR algorithms using double σ and using cost function that directly regulates the differentiation result have been proposed. The advanced SVR algorithm can make accurate curve fitting for ICA with overall error less than 1% (maximum 3%) throughout cycle lives, for four kinds of commercial lithium-ion batteries with LiFePO4 and LiNixCoyMnzO2 cathodes, making it promising to be further applied in online SOH estimation algorithms.

Fuel cells are the most clean and efficient power source for vehicles. In particular, proton exchange membrane fuel cells (PEMFCs) are the most promising candidate for automobile applications due to their rapid start-up and low-temperature operation. Through extensive global research efforts in the latest decade, the performance of PEMFCs, including energy efficiency, volumetric and mass power density, and low temperature startup ability, have achieved significant breakthroughs. In 2014, fuel cell powered vehicles were introduced into the market by several prominent vehicle companies. However, the low durability and high cost of PEMFC systems are still the main obstacles for large-scale industrialization of this technology. The key materials and components used in PEMFCs greatly affect their durability and cost. In this review, the technical progress of key materials and components for PEMFCs has been summarized and critically discussed, including topics such as the membrane, catalyst layer, gas diffusion layer, and bipolar plate. The development of high-durability processing technologies is also introduced. Finally, this review is concluded with personal perspectives on the future research directions of this area.

The polymer electrolyte membrane (PEM) fuel cell system is considered to be an ideal alternative for the internal combustion engine, especially when used on a city bus. Hybrid buses with fuel cell systems and energy storage systems are now undergoing transit service demonstrations worldwide. A hybrid PEM fuel cell city bus with a hierarchical control system is studied in this paper. Firstly, the powertrain and hierarchical control structure is introduced. Secondly, the vehicle control strategy including start-stop strategy, energy management strategy, and fuel cell control strategy, including the hydrogen system and air system control strategies, are described in detail. Finally, the performance of the fuel cell was analyzed based on road test data. Results showed that the different subsystems were well-coordinated. Each component functioned in concert in order to ensure that both safety and speed requirements were satisfied. The output current of the fuel cell system changed slowly and the output voltage was limited to a certain range, thereby enhancing durability of the fuel cell. Furthermore, the economic performance was optimized by avoiding low load conditions.