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With the continuous popularization of electric vehicles (EVs), ensuring the best performance of EVs has become a significant concern, and lithium-ion power batteries are considered as the essential storage and conversion equipment for EVs. Therefore, it is of great significance to quickly evaluate the state of power batteries. This paper investigates a fast state estimation method of power batteries oriented to after-sales and maintenance. Based on the battery equivalent circuit model and heuristics optimization algorithm, the battery model parameters, including the internal ohmic and polarization resistance, can be identified using only 30 minutes of charging or discharging process data without full charge or discharge. At the same time, the proposed method can directly estimate the state of charge (SOC) and maximum available capacity of the battery without knowing initial SOC information.

The formation is a crucial step in the production process of lithium-ion batteries (LIBs), during which the solid electrolyte interphase (SEI) is formed on the surface of the anode particles to passivate the electrode. It determines the performance of the battery, including its capacity and lifetime. A meticulously designed formation protocol is essential to regulate and optimize the stability of the SEI, ultimately achieving the optimal performance of the battery. Current research on formation protocols in lithium-ion batteries primarily focuses on temperature, current, and voltage windows. However, there has been limited investigation into the influence of different initial pressures on the formation process, and the evolution of cell pressure during formation remains unclear. In this study, a pressure-assisted formation device for lithium-ion pouch cells is developed, equipped with pressure sensors.

Alternating current (AC) heating is an efficient and homogeneous manner to warm Lithium-ion batteries (LIBs) up. The integrated design of AC heating combined with the motor drive circuit has been studied by many scholars. However, the problems of excessive heating frequency (>1kHz) and zeros torque output of the motor during the heating process have not been solved. High-frequency AC excitation may be detrimental to the battery because the effect of high-frequency AC excitation on the state of health of the battery is unknown. In addition, although the zero-torque output can be realized by controlling the q-axis current to zero, the torque ripple is still difficult to eliminate in a real-world application. To further solve the above problems, the motor’s neutral conductor is pulled out and connected to a large capacitor to increase the current amplitude of the AC heating at low frequencies.

The application of Li(Ni0.8Co0.1Mn0.1)O2 (NCM811) cathode-based lithium-ion batteries (LIBs) has alleviated electric vehicle range anxiety. However, the subsequent thermal safety issues limit their market acceptance. A detailed analysis of the failure evolution process for large-format LIBs is necessary to address the thermal safety issue. In this study, prismatic cells with nominal capacities of 144Ah and 125Ah are used to investigate the thermal runaway (TR) characteristics triggered by lateral overheating. Additionally, TR characteristics under two states of charge (SoCs) (100% and 5%) are discussed. Two cells with 100% SoC exhibit similar characteristics, including high failure temperature, high inhomogeneity of temperature distribution, multi-points jet fire, and significant mass loss. Two cells with 5% SoC demonstrate only a slight rupture of the safety valve and the emission of white smoke.

Battery safety issues have severely limited the rapid development and popularization of electric vehicles. Harsh conditions such as high temperature accelerate the degradation of battery safety. To address this issue, a comprehensive analysis of the impact of high-temperature cyclic aging on lithium-ion battery safety is carried out. In the Accelerating Rate Calorimeter, lithium-ion batteries are performed on adiabatic thermal runaway tests and overcharge tests. Regardless of the fully-charged state or half-charged state, in the adiabatic thermal runaway process, high-temperature cyclic aging reduces the characteristic temperature, and the activation energy from the self-heating temperature to thermal runaway triggering temperature decreases. During the overcharge process, high-temperature cyclic aging increases the voltage plateau and the crest voltage before thermal runaway, and their corresponding charging temperature decreases.

With the degradation of lithium-ion batteries, the battery safety performance changes, which further influences the safe working window. In this paper, the pouch ternary lithium-ion battery whose rated capacity is 4.2 Ah is used as the research object to investigate the impact of the high-temperature calendar and cyclic aging on tolerance performance. The overcharge-to-thermal-runaway test is performed on the fresh cell and aged cell (90% SOH). The inflection point of voltage for aged cells appears earlier than that of the fresh cell, while the voltage corresponding to the inflection point is the same for them, which means that the voltage at which lithium plating occurs is the same. However, the voltage plateau and the crest voltage before thermal runaway of aged cell are significantly higher than that of the fresh cell. Besides, ohmic heat, reversible heat, and side reaction heat make contribution to the thermal runaway triggering.

The cold-start of proton exchange membrane fuel cell (PEMFC) has been one of the technical challenges for fuel cell vehicle table ommercialization. In this study, a one-dimensional cold start transient model of PEMFC was developed for the transfer of water, heat, electrons and protons during the cold start process. Different loading modes, including constant voltage, constant current, and current ramping, were adopted for fuel cell cold starting analysis, respectively. The internal water-heat transfer within fuel cell was investigated under different loading modes. The results show that in the constant current mode, for the high current, the cold start process can produce more heat than other modes, which can increase fuel cell temperature rapidly. However, this process may easily fail before the ice fully covers the cathode catalyst layers (CL).

Lithium-ion batteries (LIBs) with relatively high energy, power density and eco-friendly characteristic are considered as a vital energy source in consumer market of portable electronics and transportation sector especially in electric vehicles (EVs). To meet the higher capacity requirements, the nickel-rich LIBs with higher capacity has been used as the commercial power batteries. However, the battery with higher energy density is more destructive, which could result in thermal runaway accidents and make the battery safety issues become more and more prominent. Thermal runaway of LIBs is one of the key scientific problems in safety issues. Until now, the inducement of thermal runaway process is complicated which perplex researchers and industry a lot. On the one hand, the internal mechanism about thermal runaway should be deeply studied. On the other hand, in-situ monitoring should be developed to supply the mechanism study and early warning.

The proton exchange membrane fuel cell (PEMFC) system has emerged as the state-of-art power source for the electric vehicle, but the widespread commercial application of fuel cell vehicle is restricted by its short service life. An enabling high accuracy model holds the key for better understanding, simulation, analysis, subsystem control of the fuel cell system to extract full power and prolong the lifespan. In this paper, a quasi-dynamic lumped parameters model for a 3kW stack is introduced, which includes filling-and-emptying volume sub-models for the relationships between periphery signals and internal states, static water transferring sub-model for the membrane, and empirical electrochemical sub-model for the voltage response. Several dynamic experiments are carried out to identify unknown parameters of the model.

As the core component of electric vehicles (EVs), batteries attach increasingly general attention along with the rapid expansion of electric vehicle market. Battery performance effect directly the safety and reliability of the EVs, so its managing technologies are more and more crucial. Among them, the methods of estimating the state of health (SoH) and predicting remaining useful life become the focuses, which are essential to ensure their dependability and optimum performance over time. This paper mainly focuses on impedance modeling and aging research (aging diagnosis and life prediction) of lithium-ion batteries. Electrochemical impedance spectroscopy (EIS) technique is used to obtain impedance characteristic of batteries. On the one hand, equivalent circuit modeling (ECM) can be motivated by EIS, with the goal to fit measured impedance data using circuit elements.

State-of-Power (SoP) prediction of Li-ion battery is necessary in battery management system for electric vehicles in order to deal with limited conditions, prevent overcharge and over discharge situations, increase the life of the battery and provide effective battery operation. This article suggests a method to on-line predict the 10-s charge and discharge peak power of Li-ion battery by twice recursions. First with the dynamic battery model we use the first recursion based on a least square method to get parameters which are influenced by the state of charge of Li-ion battery and temperature, etc. The dynamic model is an equivalent circuit model. Current and voltage are input online into the battery model. By recursive least square method the parameters are updated in real time. Moreover, when we use a recursive method to get real-time parameters, we add an extra proper factor to abandon old datum, which increases the real-time capability of state-of-power prediction.

Regarding the lithium-ion batteries used in the electric vehicle, charging time and charging efficiency are the concern of the public. In this paper, a lot of experiments were conducted to investigate the common charging strategies, including the CC-CV (constant current-constant voltage) charging and the pulse current charging, for the LiFePO4 batteries, which are still widely used in commercial vehicles. Charging temperature and the charging current in the CC phase are the main influence factors to be studied for the CC-CV charging strategy, and the contribution of the CC phase and CV phase to the whole charging is analyzed from three aspects, including the time percent, charging energy efficiency and the capacity of battery at different temperatures and charging current.

Range anxiety problem has always been one of the biggest concern of consumers for pure electric vehicles. Accurate driving range prediction is based on accurate lithium-ion battery pack SOC (State of Charge) estimation. In this article, a complete SOC estimation algorithm is proposed from cell level to battery pack level. To begin with, the equivalent circuit model (ECM) is applied as the model of battery cell. ECM parameters are identified every 10% SOC interval through genetic algorithm. The dual extended Kalman filtering (DEKF) algorithm is adopted for cell-level SOC and ohmic resistance R0 estimation. The estimation accuracy of cell SOC and R0 is verified under NEDC dynamic working condition. The cell-level SOC estimation error is below 1%. However, cell inconsistency can always result in inaccurate cell SOC estimation inside the battery pack. The impact of initial SOC inconsistency and internal resistance inconsistency between cells on battery pack SOC is specifically analyzed.

With the rise of new energy vehicles, lithium-ion batteries have been widely used. However, the safety, cruising range and practicality of electric vehicles are still major obstacles to their development. Among them, the low-temperature performance of electric vehicles is receiving more and more attention. Lithium-ion batteries have poor low-temperature performance. At low temperatures, not only the charging efficiency is lowered, but also the energy that can be flushed is correspondingly reduced, thereby resulting in a decrease in capacity and an increase in aging. At present, the mechanism and influence factors of battery discharge aging have been studied relatively well, but there are few researches on low temperature charging aging of batteries.

Electrochemical Impedance Spectroscopy (EIS) is often applied to analyze and describe the battery internal electrochemical processes. And the methods of battery state estimation including state of health diagnosis with electrochemical impedance have attracted a wide attention. In the paper, a novel fast impedance measuring method based on wavelet transform with a step excitation current is proposed and further studied. With the method, the current generated by the electric vehicle and the responding voltage of the battery can be utilized to calculate and provide the battery impedance for the battery management system. Taking into account the varying amplitude of the current and the battery states, the battery impedance was measured with step excitation signals of different amplitude at different temperature and state of charge (SOC).

Battery models are often applied to describe the dynamic characteristics of batteries and can be used to predict the state of the battery. Due to the process of charging and discharging, the battery heat generation will cause the inhomogeneity between inner battery temperature and surface temperature. In this paper, a novel battery impedance model, which takes the impact of the battery internal temperature gradient on battery impedance into account, is proposed to improve the battery model performance. Several experiments are designed and conducted for pouch typed battery to investigate the electrochemical impedance spectroscopy (EIS) characteristics with the artificial temperature gradient (using a heating plate). Experimental results indicate that the battery internal temperature gradient will influence battery EIS regularly.

Lithium ion battery is considered as one of the most possible energy storage equipment for new energy vehicles (EV, HEV, etc.) because of the advantages of long cycle life, high power density and low self-discharge rate. However, under freezing condition high power battery suffers of significant performances losses. For example, they would suffer from significant power capability losses and poor rate performance, which would restrict the availability to delivery or to gain of high current in transient conditions. To evaluate those performance drawbacks and to make an efficient design, good mathematical models are required for system simulation especially for battery thermal management. In this paper, a three-dimensional homogenization thermal model of a 20 Ah prismatic lithium ion battery with LiFePO4 (LFP) cathode is described.

Parallel-connected modules have been widely used in battery packs for electric vehicles nowadays. Unlike series-connected modules, the direct state inconsistency caused by parameter inconsistency in parallel modules is current and temperature non-uniformity, thus resulting in the inconsistency in the speed of aging among cells. Consequently, the evolution pattern of parameter inconsistency is different from that of series-connected modules. Since it’s practically impossible to monitor each cell’s current and temperature information in battery packs, considering cost and energy efficiency, it’s necessary to study how the parameter inconsistency evolves in parallel modules considering the initial parameter distribution, topology design and working condition. In this study, we assigned cells of 18650 format into several groups regarding the degree of capacity and resistance inconsistency. Then all groups are cycled under different environmental temperature and current profile.

To monitor and guarantee batteries of electric vehicles in normal operation, battery models should be established primarily for the further application in battery management system such as parameter identification and state estimation including state of charge (SOC), state of health (SOH) and so on. In this paper, an improved battery modeling method is proposed which is based on the recursive least square (RLS) algorithm employing an optimized objective function. The proposed modified objective function not only includes the normal sum of voltage error squares between measured voltage and model output voltage but also introduces a new variable representing the sum of first order difference error squares for both kinds of voltages. This specialty can undoubtedly guarantee better agreement for the measured output and the model output. The battery model used in this paper is selected to be the conventional second order equivalent circuit model.

An alternating current (AC) heating method for a NMC lithium-ion battery with 8Ah capacity is proposed. The effects of excitation frequency, current amplitudes, and voltage limit condition on the temperature evolution are investigated experimentally. Current amplitudes are set to 24A(3C), 40(5C), and 64A(8C), and excitation frequencies are set to 300Hz, 100Hz, 30Hz, 10Hz, 5Hz, and 1Hz respectively. The voltage limitations are necessary to protect cells from overcharge and over-discharge. Therefore the voltage limit condition (4.2V/2.75V, 4.3V/2.65V, and 4.4V/2.55V) are also considered in depth to verify the feasibility of the AC heating method. The temperature rises prominently as the current increases, and the decrement of frequencies also lead to the obvious growth of battery temperature. The battery obtain the maximum temperature rise at 64A and 1Hz, which takes 1800s to heat up the battery from -25°C to 18°C.