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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.

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.

High-nickel lithium-ion batteries extend the driving mileage of electric vehicles (EVs) to 600km without much cost increment. However, thermal accidents commonly occur due to their poor thermal stability, such as thermal runaway. To address the issue, a comprehensive analysis of the thermal runaway behavior of high-nickel lithium-ion batteries with different specifications is conducted. The thermal runaway process is divided into five stages based on self-heating generation, voltage drop, safety valve rupture, and thermal runaway triggering for the three tested cells. The three tested cells demonstrate similar behaviors during each stage of the thermal runaway process. However, there are still apparent differences between their characteristics. This study analyses the thermal runaway features from the following aspects: (i) characteristic temperature; (ii) the relationship between sudden voltage drop and characteristic temperatures; (iii) temperature recovery; (iv) thermodynamics.

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).

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.

As a key parameter of fundamental electrical, mutual inductance always used to characterize the overall performance of wireless power transfer (WPT) system in electric vehicle charging applications. However, in real case, factors such as parking misalignments, load variations and intrusion of foreign objects would result in a variation of mutual inductance between both coils, which may adversely impact on transmitted power and transmission efficiency. Therefore, in this paper, we propose to identify mutual inductance parameter with the least square method (LSM) based on the equivalent circuit model. In section II, COMSOL is adopted to simulate mutual inductance variation with the change of lateral offset. In section III, the differential equation is derived from the state space equation of the WPT system. Through identifying the process parameters, the mutual inductance of coils can be obtained by the functional relationship between them.

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.

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).

For balancing Li-ion battery cells connected in series and effectively improving the consistency of the cells, a bi-directional equalization system based on fly-back transformer is proposed. Unlike the passive equalization technology using a resistor or active equalization with expensive DC-DC converter for the balancing among the cells, this equalization circuit consists of the fly-back transformer and RCD circuit, which can easily and cheaply realize the energy transfer between the whole battery module and the cells, and thus achieving bidirectional equalization. In this system, both the primary side and the secondary side of multi-winding transformer are connected to a MOSFET. All MOSFETs are controlled by the PWM signal. The control timing and duty ratio of the PWM control signal are determined through the simulation analysis. Meanwhile, an RCD circuit is applied at the primary side of multi-winding transformer for buffering the peak voltage caused by leakage inductance.

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.

Wireless charging system for vehicular power batteries is becoming more and more popular. As one of important issues, charging power regulation is indispensable for online control, especially when the distance or angle between chassis and ground changes. This paper proposes a novel power regulation method named Z-Source-Based Pulse-Amplitude-Modulation (ZSB-PAM), which has not been mentioned in the literatures yet. The ZSB-PAM employs a unique impedance network (two pairs of inductors and capacitors connected in X shape) to couple the cascaded H Bridge to the power source. By controlling the shoot-through state of H bridge, the input voltage to H bridge can be boosted, thus the transmitter current can be adjusted, and hence, charging current and power for batteries. A LCL-LCL resonant topology is adopted as the main transfer energy carrier, for it can work with a unity power factor and have the current source characteristic which is suitable for battery charging.

This paper aims at accurately modeling the nonlinear hysteretic relationship between open circuit voltage (OCV) and state of charge (SOC) for LiFePO4 batteries. The OCV-SOC hysteresis model is based on the discrete Preisach approach which divides the Preisach triangle into finite squares. To determine the weight of each square, a linear function system is constructed including a series of linear equations formulated at every sample time. This function system can be solved by computer offline. When applying this approach online, the calculated square weight vector is pre-stored in advance. Then through multiple operations with hysteresis state vector of squares updated online at every sampling time, the SOC considering the influence of OCV-SOC hysteresis is predicted.