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

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

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

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

Micro short circuit (MSC) is a potential risk of thermal runaway in batteries. It is essential to prevent thermal runaway and improve the safety of batteries via short-circuit detection. Traditional detection methods take the healthy cells in the battery pack as a reference, which use statistical characteristics to perform qualitative or quantitative MSC diagnosis. However, for the application scenarios of one single cell, the existing methods cannot determine short circuit due to the lack of healthy batteries as a reference. Therefore, a quantitative diagnosis method for single lithium-ion cell applications is proposed in this paper. The core idea of the method is that the estimated capacity of the short-circuit cell during the discharging process is smaller than the normal value, while the estimated capacity during the charging process is larger than the normal value. Hence, by comparing the historical capacity variation characteristics under the charging and discharging cycle, the fault can be diagnosed quantitatively. The experimental results show when the short-circuit resistance is 5Ω for large-capacity cells, the short-circuit resistance estimation accuracy can reach 2.5%. And the capacity estimation error of the short-circuit cell is within 1.5% after the capacity is compensated.

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

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

In a fuel cell vehicle, a direct current boost converter (DCC) is required to link a polymer electrolyte membrane fuel cell system (FCS) and lithium battery packages. The DCC is installed to regulate the output power of the FCS, and can be controlled in different ways, via current, voltage, or power modes. Interactions between a DCC and FCS have attracted growing interests in recent years, because they affect dynamic and stable performances of the entire system. This paper outlines a simulation study on interactions between high-frequency switching operations of a DCC and internal states of an FCS based on a multiscale model. Results are as follows. (1) High-frequency switching operations have a major influence on the cathode overpotential, voltage ohmic loss and water transport through the membrane, whereas the influence on the partial pressures of gas species inside the stack is slight. (2) The FCS is more stable in the case of membrane dehydration than in that of water flooding. DCC's control mode has a greater influence on the FCS when water flooding occurs than membrane dehydration. The power control mode is the most unstable of the three, whereas the current control mode is the most stable.

State-of-charge (SOC) inconsistency impacts the power, durability and safety of the battery pack. Therefore, it is necessary to measure the SOC inconsistency of the battery pack with good accuracy. We explore a novel method for modeling and estimating the SOC inconsistency of lithium-ion (Li-ion) battery pack with low computation effort. In this method, a second-order RC model is selected as the cell mean model (CMM) to represent the overall performance of the battery pack. A hypothetical Rint model is employed as the cell difference model (CDM) to evaluate the SOC difference. The parameters of mean-difference model (MDM) are identified with particle swarm optimization (PSO). Subsequently, the mean SOC and the cell SOC differences are estimated by using extended Kalman filter (EKF). Finally, we conduct an experiment on a small Li-ion battery pack with twelve cells connected in series. The results show that the evaluated SOC difference is capable of tracking the changing of actual value after a quick convergence.

Sate of charge (SOC) estimation is generally acknowledged as one of the most important functions in battery management system for lithium-ion batteries in new energy vehicles. Though every effort is made for various online SOC estimation methods to reliably increase the estimation accuracy as much as possible within the limited on-chip resources, little literature discusses the error sources for those SOC estimation methods. This paper firstly reviews the commonly studied SOC estimation methods from a conventional classification. A novel perspective focusing on the error analysis of the SOC estimation methods is proposed. SOC estimation methods are analyzed from the views of the measured values, models, algorithms and state parameters. Subsequently, the error flow charts are proposed to analyze the error sources from the signal measurement to the models and algorithms for the widely used online SOC estimation methods in new energy vehicles. Finally, with the consideration of the working conditions, choosing more reliable and applicable SOC estimation methods is discussed, and the future development of the promising online SOC estimation methods is suggested.

City buses using polymer electrolyte membrane (PEM) fuel cells are considered to be the most likely fuel cell vehicles to be commercialized in China. The technical specifications of the fuel cell systems (FCSs) these buses are equipped with will differ based on the powertrain configurations and vehicle control strategies, but can generally be classified into the power-follow and soft-run modes. Each mode imposes different levels of electrochemical stress on the fuel cells. Evaluating the aging behavior of fuel cell stacks under the conditions encountered in fuel cell buses requires new durability test protocols based on statistical results obtained during actual driving tests. In this study, we propose a systematic design method for fuel cell durability test protocols that correspond to the power-follow mode based on three parameters for different fuel cell load ranges. The powertrain configurations and control strategy are described herein, followed by a presentation of the statistical data for the duty cycles of FCSs in one city bus in the demonstration project. Assessment protocols are presented based on the statistical results using mathematical optimization methods, and are compared to existing protocols with respect to common factors, such as time at open circuit voltage and root-mean-square power.

Layered Li[NixCoyMn1-x-y]O2 (NCM) cathodes with high capacity are poor in thermal stability and cause safety concerns on their batteries. Although the thermal features of various NCM materials have been widely described, detailed kinetics for thermal modeling still remain unknown. Here, we conducted thermal stability tests and for the first time quantified the thermal kinetics related to the three-stage phase transition process of the delithiated NCM cathodes with different nickel contents and particle structures. Results show that NCM cathodes with higher nickel ratios and polycrystal (PC) structures display worse thermal stability. Enhancing nickel contents from 0.33 to 0.8 leads to a 50 °C drop in the first thermal reaction temperature and up to 60% decrease in the activation energy, with enthalpy two times larger. The influence of particle structure is less significant. PC materials show up to a 10% drop in activation energy, with total enthalpy increasing by 25 J when compared with single-crystal materials. Finally, the thermal kinetics are combined with a widely-used battery thermal runaway (TR) model. The model simulation results predict the battery TR tipping point at x = 0.5. The detailed kinetics obtained in this study can further contribute to the development of battery thermal models and prompt the applications of NCM materials.

The anode potential inside the lithium-ion battery is crucial for battery internal state observation in electric vehicles since it indicates the states of lithium deposition at the anode surface. Conventional equivalent circuit models (ECMs) used in the battery management system (BMS) can only predict limited battery external characteristics. Electrochemical models which can simulate the battery internal behaviors are too complex for onboard applications. In this paper, a novel decomposed electrode model (DEM) is proposed for real-time anode potential observation in real BMSs. The cathode and anode parameters can be calibrated by inserting a reference electrode inside the battery. The decomposed electrode model shows high accuracy in predicting the cell terminal voltage and the anode potential under various current conditions. This model also has predominance considering both model simplicity and computational efficiency. An online anode potential observation algorithm is developed based on the DEM and the extended Kalman filter. A lithium plating-free fast charging algorithm is formulated further by integrating the closed-loop potential observer and the closed-loop current controller. The result indicates that the proposed decomposed electrode model is suitable for online applications in the BMS.

Intentionally inducing worst-case thermal runaway scenarios in Lithium-ion batteries on-demand is a definitive way to test the efficacy of battery systems in safely mitigating the consequences of catastrophic failure. This study investigates the combined impact of heating power and heating area on thermal runaway triggering. Two different heating powers and four incremental heating areas constitute eight heating schemes in the experimental test. A 3D model is built in Comsol to satisfy the experimental result and investigate the heat transfer thermal runaway mechanism induced by external heating. The results indicate that when the heating power is the same, the smaller heating area that has higher heating power density can trigger TR quicker. The heater produces less heating energy, and less flux energy will be introduced into the battery. Thermal runaway prediction and recommended heating scheme map is proposed based on simulation result. The heating scheme with both high heating power and the small heating area has the greatest ability on shortening the heating time. The flux energy and its equivalent flux power pass through the interface between heater and battery is used to construct a comprehensive description of thermal runaway mechanism induced by heating method.

This paper presents an electrochemical-thermal coupled overcharge-to-thermal-runaway (TR) model to predict the highly interactive electrochemical and thermal behaviors of lithium ion battery under the overcharge conditions. In this model, the battery voltage equals the difference between the cathode potential and the anode potential, whereas the temperature is predicted by modeling the combined heat generations, including joule heat, thermal runaway reactions and internal short circuit. The model can fit well with the adiabatic overcharge tests results at 0.33C, 0.5C and 1C, indicating a good capture of the overcharge-to-TR mechanism. The modeling analysis based on the validated model helps to quantify the heat generation rates of each heat sources during the overcharge-to-TR process. And the two thermal runaway reactions including the electrolyte oxidation reaction and the reaction between deposited lithium and electrolyte are found to contribute most to the heat generations during the overcharge process. Further modeling analysis on the critical parameters is performed to find possible solutions for the overcharge problem of lithium ion battery. The result shows that increasing the oxidation potential of the electrolyte, and increasing the onset temperature of thermal runaway are the two effective ways to improve the overcharge performance of lithium ion battery.

As one single cell cannot meet power and driving range requirement in an electric vehicle, the battery packs with hundreds of single cells connected in parallel and series should be constructed. The most significant difference between a single cell and a battery pack is cell variation. Not only does cell variation affect pack energy density and power density, but also it causes early degradation of battery and potential safety issues. The cell variation effects on battery packs are studied, which are of great significant to battery pack screening and management scheme. In this study, the description for the consistency characteristics of battery packs was first proposed and a pack model with 96 cells connected in series was established. A set of parameters are introduced to study the cell variation and their impacts on battery packs are analyzed through the battery pack capacity loss simulation and experiments. Meanwhile, the capacity loss composition of the battery pack is obtained and verified by the temperature variation experiment. The results from this research can demonstrate that the temperature, self-discharge rate and coulombic efficiency are the major affecting parameters of cell variation and indicate the dissipative cell equalization is sufficient for the battery pack.

A scheme for designing a second-order sliding-mode (SOSM) observer that estimates critical internal states on the cathode side of a polymer electrolyte membrane (PEM) fuel cell system is presented. A nonlinear, isothermal dynamic model for the cathode side and a membrane electrolyte assembly are first described. A nonlinear observer topology based on an SOSM algorithm is then introduced, and equations for the SOSM observer deduced. Online calculation of the inverse matrix produces numerical errors, so a modified matrix is introduced to eliminate the negative effects of these on the observer. The simulation results indicate that the SOSM observer performs well for the gas partial pressures and air stoichiometry. The estimation results follow the simulated values in the model with relative errors within ± 2% at stable status. Large errors occur during the fast dynamic processes (

The transient water transport problem within polymer electrolyte membrane fuel cell (PEMFC) makes its dynamic behavior unpredictable occasionally, bringing uncertainties to the water management. In this work, a novel experimental methodology is proposed, where the dynamic processes of gas diffusion layer (GDL) imbibition-drainage are accomplished by galvanostatic discharge and alterable air feeding, meanwhile, the PEMFC voltage characteristic is measured as an external state to derive stoichiometry-voltage curve (λ-V curve). The other transient processes within PEMFC, including membrane sorption/desorption, catalyst surface covering/uncovering and gas channel blocking/unblocking, are also analyzed by high frequency resistance (HFR) measurement, air & oxygen feeding comparison, and pressure drop measurement, respectively. Taking a commercial PEMFC for example, the liquid water's transient capillary transport within GDL is found to dominate PEMFC's λ-V hysteresis under the condition of high humidification. For the first time, three cycles of GDL imbibition/drainage are in-situ accomplished on the PEMFC, demonstrating: (a) V(λ) during 1st imbibition is lower than V(λ) during 2nd and 3rd imbibition, which agree closely, implying ‘water pathways’ built after 1st imbibition improve PEMFC's performance. (b) V(λ) during drainage is always higher than V(λ) during imbibition, despite the steady-existing ‘water pathways’. (c) ‘Water pathways’ in GDL are established within 24 min.

The continuous chemical degradation of the polymer membrane in polymer electrolyte fuel cells causes membrane pinhole formation, therefore substantial increase in the gas cross-over flux. In this paper, a numerical method to simulate the pinhole formation process during chemical degradation is proposed. The method assumes that a membrane pinhole is formed by connected local micro-pores, which are generated owing to the mass loss during chemical membrane degradation. In addition, pinhole formation is combined with reduction of the membrane thickness to account for the membrane structure changes. A dynamic chemical degradation model that incorporates the effects of membrane structure changes is validated against the experimental results of the accelerated stress test. Results confirm the proposed method increases the accuracy of the chemical degradation model in predicting the macroscopic properties, including cumulative mass loss, hydrogen cross-over and open circuit voltage. Formation of pinhole starts from the anode, where degradation is more severe, and propagates to the cathode. The model also systemically studies the effects of temperature, pressure, and relative humidity on chemical degradation. The degradation rate increases with elevated temperatures and pressures. The degradation is shown to be more severe at moderate relative humidity, and the comparison with experimental findings are discussed.

Microgrid-based electric vehicle (EV) fast charging station is believed to be a promising solution to lessen the enormous charging burden of large-scale EVs on the main grid, while the optimal charging protocol in microgrids is still missing. Thus, this paper presents a model-based continuous differentiable charging (CDC) approach for EV fasting charging in microgrids. The priority of the proposed method on bus voltage regulation over the conventional multi-stage constant current (MCC) strategy is first validated. The parameters of the CDC profile are obtained through the model-based particle swarm optimization framework. An electrothermal-coupled equivalent circuit model is adopted as the performance model while a physical-based semi-empirical battery degradation model is built to measure the capacity loss and lithium plating rate. The objective function of the optimization is to reduce the charging time, capacity fading, and bus voltage disturbance, with the constraints of preventing lithium deposition and limiting the maximum voltage and temperature. The optimization results of the CDC and MCC methods confirm that CDC protocol can reduce the charging time by about 33.5% without scarifying battery health. The sensitivity analyses of initial state of charge, ambient temperature, and heat dissipation coefficient further suggest the universality of the proposed CDC strategy.

The powertrain plays an essential role in improving the tractive performance and the fuel consumption of fuel cell hybrid vehicles. This paper presents a comparative study of two different powertrains for fuel cell hybrid buses. The significant difference between the two powertrains lies in the types and arrangements of the electrical motor. One powertrain employs an induction motor to drive the vehicle, while the other powertrain adopts two permanent magnetic synchronous motors for near-wheel propulsion. Besides, the tiny difference between the proposed powertrain is the supply path of the fuel cell accessories, which can have an effect on the powertrain efficiency. The component parameters and energy management strategies for the two powertrain are determined. The fuel cell hybrid buses equipped with the two powertrains are developed, and some road tests are achieved, according to the chosen procedures or driving cycles. The paper focuses on the tractive performance and energy analysis of the powertrains based on the testing results. Finally, the paper summarizes the relative merits of the proposed powertrains.

Packs with high self-discharge accelerate the capacity decline and even cause the safe issues. It is important to keep the self-discharge rate at a uniform and small level for all the cells in a pack. The traditional clustering methods are either costly or time-consuming. In this paper, a novel classification method is invented to realize the fast estimation of the relative self-discharge rate. Firstly, the balancing technology for large-scale cells is proposed to ensure the voltage equalization. Small batches of equalized cells are then clamped in detection circuits to realize the external mapping of the internal current originated from the self-discharge. The relative self-discharge rates of the cells are estimated by the equivalent parallel circuit model with self-discharge. The results show that the method can detect the cell with a relative self-discharge rate of 3%/month for three cells at a time within 1 h. To further classify the cells according to the relative self-discharge rate, microammeters are connected in each branch of the detection circuits. The results show that the method can identify the relative self-discharge rate in less than 2 h, and the errors can be kept within 0.1%/month.

Internal short circuit (ISCr) detection of a battery is critical for preventing thermal runaway and enhancing electrical vehicle safety. In this paper, the electrical characteristics of the ISCr of a large format lithium ion battery are analyzed using the equivalent circuit model (ECM). An ISCr detection method is developed based on battery consistency within the battery pack. The ISCr detection method employs the recursive least square (RLS) algorithm based on the mean-difference model (MDM), which is derived from the ECMs consisting of the mean and difference value of cells' voltage and resistance. The algorithm first estimates the basic parameters of the MDM. Then the algorithm calculates the characteristic parameters, such as the differential of the voltage and the fluctuation function of the internal resistance, derived from the basic parameters of the MDM. These characteristic parameters obviously vary once ISCr occurs, thereby helping the battery management system to realize ISCr detection. The effectiveness of the ISCr detection method is validated through a series of experiments.

Coulombic efficiency (CE) is an important parameter for battery cells. Unfortunately, precise measurement of CE is extremely difficult, so CE is seldom focused during cell performance study. Nevertheless, correlation between stage of charge (SOC) and CE has significant impact on battery pack performance, while the influences of different correlations between SOC and CE on battery packs have never been explored as far as we know. We present a novel method which implements two series-connected cells that can determine the correlation between SOC and CE. The experimental result shows that CE is almost invariant with SOC changes for the experimental commercial LiFePO 4 /C cells. This paper further investigates series-connected cells with three typical correlations between SOC and CE in simulations. The simulation results indicate that cells with negative correlations between CE and SOC are preferred for series-connected battery packs, as they tend to diminish SOC difference and increase the pack capacity which can be considered as a self-balancing mechanism.

Charging procedures at low temperatures severely shorten the cycle life of lithium ion batteries due to lithium deposition on the negative electrode. In this paper, cycle life tests are conducted to reveal the influence of the charging current rate and the cut-off voltage limit on the aging mechanisms of a large format LiFePO 4 battery at a low temperature (−10 °C). The capacity degradation rates accelerate rapidly after the charging current reaches 0.25 C or the cut-off voltage reaches 3.55 V. Therefore the scheduled current and voltage during low-temperature charging should be reconsidered to avoid capacity degradation. Lithium deposition contributes to low-temperature aging mechanisms, as something needle-like which might be deposited lithium is observed on the surface of the negative electrode after disassembling the aged battery cell. To confirm our explanation, incremental capacity analysis (ICA) is performed to identify the characteristics of the lithium deposition induced battery aging mechanisms. Furthermore, the aging mechanism is quantified using a mechanistic model, whose parameters are estimated with the particle swarm optimization algorithm (PSO). The loss of reversible lithium originating from secondary SEI formation and dead lithium is confirmed as the cause of the aging.

This paper investigates the overcharge-induced capacity fading behavior of large format lithium-ion batteries with Li y Ni 1/3 Co 1/3 Mn 1/3 O 2 + Li y Mn 2 O 4 composite cathode. The capacity degradation mechanism is studied using a prognostic/mechanistic model and incremental capacity analysis (ICA). The overcharge process can be divided into four stages. Loss of active material (LAM) in both the cathode and the anode and loss of lithium inventory (LLI) in different overcharge stages are quantified using the prognostic/mechanistic model. In Stage I, the battery shows no obvious capacity degradation until it is overcharged to 120% state of charge (SOC). In Stage II, LLI occurs as a result of lithium deposition, with LAM in the Li y Mn 2 O 4 of the composite cathode. Internal resistance increases in Stage II indicating the thickening of the SEI film. In Stage III, LAM in both the cathode and the anode happen as the battery is overcharged to over 140% SOC. The battery starts to swell in this stage, as a result of the electrolyte oxidation. In Stage IV, the battery ruptures, with all the stored energy releasing instantly due to internal short circuit. Pinholes on the separator surface are observed after dissembling the batteries that are overcharged to 150% SOC or more.

Battery cell capacity loss is extensively studied so as to extend battery life in varied applications from portable consumer electronics to energy storage devices. Battery packs are constructed especially in energy storage devices to provide sufficient voltage and capacity. However, engineering practice indicates that battery packs always fade more critically than cells. We investigate the evolution of battery pack capacity loss by analyzing cell aging mechanisms using the “Electric quantity – Capacity Scatter Diagram (ECSD)” from a system point of view. The results show that cell capacity loss is not the sole contributor to pack capacity loss. The loss of lithium inventory variation at anodes between cells plays a significant role in pack capacity evolution. Therefore, we suggest more attention could be paid to the loss of lithium inventory at anodes in order to mitigate pack capacity degradation.

Now the lithium ion batteries are widely used in electrical vehicles (EV). The battery modeling and state estimation is of great significance. The rigorous physic based electrochemical model is too complicated for on-line simulation in vehicle. In this work, the simplification of physics-based model lithium ion battery for application in battery management system (BMS) on real electrical vehicle is proposed. Approximate method for solving the solid phase diffusion and electrolyte concentration distribution problems is introduced. The approximate result is very close to the rigorous model but fewer computations are needed. An extended single particle model is founded based on these approximated results and the on-line state of charge (SOC) estimation algorithm using the extended Kalman filter with this single particle model is discussed. This SOC estimation algorithm could be used in the BMS in real vehicle.

This paper investigates the mechanisms of penetration induced thermal runaway (TR) propagation process within a large format lithium ion battery pack. A 6-battery module is built with 47 thermocouples installed at critical positions to record the temperature profiles. The first battery of the module is penetrated to trigger a TR propagation process. The temperature responses, the voltage responses and the heat transfer through different paths are analyzed and discussed to characterize the underlying physical behavior. The temperature responses show that: 1) Compared with the results of TR tests using accelerating rate calorimetry (ARC) with uniform heating, a lower onset temperature and a shorter TR triggering time are observed in a penetration induced TR propagation test due to side heating. 2) The maximum temperature difference within a battery can be as high as 791.8 °C in a penetration induced TR propagation test. The voltage responses have a 5-stage feature, indicating that the TR happens in sequence for the two pouch cells packed inside a battery. The heat transfer analysis shows that: 1) 12% of the total heat released in TR of a battery is enough to trigger the adjacent battery to TR. 2) The heat transferred through the pole connector is only about 1/10 of that through the battery shell. 3) The fire has little influence on the TR propagation, but may cause significant damage on the accessories located above the battery. The results can enhance our understandings of the mechanisms of TR propagation, and provide important guidelines in pack design for large format lithium ion battery.

The estimation of battery remaining discharge capacity ( Q RDC ) is essential for the remaining driving range prediction on pure electric vehicles. A traditional Q RDC estimation method is based on the determination of battery state of charge (SOC), in which the estimation accuracy could be affected by the variation in discharge conditions. In this research, a novel Q RDC estimation method through differential voltage (d V /d Q ) analysis is introduced for lithium-ion batteries. Through analyzing the characteristics of terminal voltage variation, the present Q RDC could be estimated by the d V /d Q value, which is capable to provide an accurate estimation result under various discharge conditions. On a commercial lithium-ion battery, the d V /d Q method is implemented for Q RDC estimation under pulse discharge profiles and dynamic profiles. The result shows that the d V /d Q method could provide accurate Q RDC estimation results under various discharge profiles in the latter part of the discharge process, and the Q RDC estimation accuracy could hence be improved by combining the differential voltage analysis with the SOC-based method. Owing to the simple computation process, the d V /d Q -based estimation method is very competitive in onboard applications.

In this paper, four different semi-active hybrid energy storage systems (HESSs), which use both supercapacitors (SCs) and batteries, are compared based on an electric city bus running the China Bus Driving Cycle (CBDC). The SC sizes of the different HESS topologies are optimized by using the dynamic programming (DP) approach, based on a dynamic degradation model of the LiFePO4 battery. The operation costs of different HESSs, including the electricity and the battery degradation costs over a whole CBDC, are minimized in the optimization process. Based on the DP results, near-optimal control strategies of different HESSs for on-line uses are proposed. Finally, the four HESS topologies are comprehensively compared from different aspects, including operation cost, initial cost, and DC bus voltage variation. Simulation results show that all HESS topologies have their merits and drawbacks, and can be used in different applications with different requirements. In addition, about 50% of the operation cost of the energy storage system is reduced by the semi-active HESSs when compared to the battery-only topology. Thus the effectiveness of adopting the SC in the HESS is verified.

This paper provides a study on the characterizations of large format lithium ion battery cells exposed to extreme high temperature but without thermal runaway. A unique test is set up: an extended volume-accelerating rate calorimetry (EV-ARC) test is terminated at a specific temperature before thermal runaway happens in the battery. The battery was cooled down after an EV-ARC test with early termination. The performances of the battery before and after the EV-ARC test are investigated in detail. The results show that (a) the melting point of the separator dictates the reusability of the 25 Ah NCM battery after a near-runaway event. The battery cannot be reused after being heated to 140 °C or higher because of the exponential rise in ohmic resistance; (b) a battery can lose up to 20% of its capacity after being heated to 120 °C just one time; (c) if a battery is cycled after a thermal event, its lost capacity may be recovered partially. Furthermore, the fading and recovery mechanisms are analyzed by incremental capacity analysis (ICA) and a prognostic/mechanistic model. Model analysis confirms that the capacity loss at extremely high temperature is caused by the increase of the resistance, the loss of lithium ion (LLI) at the anode and the loss of active material (LAM) at the cathode.

In order to predict the battery remaining discharge energy in electric vehicles, an accurate onboard battery model is needed for the terminal voltage and state of charge (SOC) estimation in the whole SOC range. However, the commonly-used equivalent circuit model (ECM) provides limited accuracy in low-SOC area, which hinders the full use of battery remaining energy. To improve the low-SOC-area performance, this paper presents an extended equivalent circuit model (EECM) based on single-particle electrochemical model. In EECM, the solid-phase diffusion process is represented by the SOC difference within the electrode particle, and the terminal voltage is determined by the surface SOC (SOC surf ) representing the lithium concentration at the particle surface. Based on a large-format lithium-ion battery, the voltage estimation performance of ECM and EECM is compared in the entire SOC range (0–100%) under different load profiles, and the genetic algorithm is implemented in model parameterization. Results imply that the EECM could reduce the voltage error by more than 50% in low-SOC area. The SOC estimation accuracy is then discussed employing the extended Kalman filter, and the EECM also exhibits significant advantage. As a result, the EECM is very potential for real-time applications to enhance the voltage and SOC estimation precision especially for low-SOC cases.

Now the lithium ion batteries are widely used in electric vehicles (EV). The cycle life is among the most important characteristics of the power battery in EV. In this report, the battery cycle life experiment is designed according to the actual working condition in EV. Five different commercial lithium ion cells are cycled alternatively under 45 °C and 5 °C and the test results are compared. Based on the cycle life experiment results and the identified battery aging mechanism, the battery cycle life models are built and fitted by the genetic algorithm. The capacity loss follows a power law relation with the cycle times and an Arrhenius law relation with the temperature. For automotive application, to save the cost and the testing time, a battery SOH (state of health) estimation method combined the on-line model based capacity estimation and regular calibration is proposed.

In this paper, the thermal runaway features of a 25 Ah large format prismatic lithium ion battery with Li(NixCoyMnz)O2 (NCM) cathode are evaluated using the extended volume-accelerating rate calorimetry (EV-ARC). 4 thermocouples are set at different positions of the battery. The temperature inside the battery is 870 °C or so, much higher than that outside the battery. The temperature difference is calculated from the recorded data. The temperature difference within the battery stays lower than 1 °C for 97% of the test period, while it rises to its highest, approximately 520 °C, when thermal runaway happens. The voltage of the battery is also measured during the test. It takes 15–40 s from the sharp drop of voltage to the instantaneous rise of temperature. Such a time interval is beneficial for early warning of the thermal runaway. Using a pulse charge/discharge profile, the internal resistance is derived from the quotient of the pulse voltage and the current during the ARC test. The internal resistance of the battery increases slowly from 20 mΩ to 60 mΩ before thermal runaway, while it rises to 370 mΩ when thermal runaway happens indicating the loss of the integrity of the separator or the battery swell.

Solvothermal approach is used to synthesize LiMn 0.9 Fe 0.1 PO 4 (LMFP) nanomaterial for Li-ion batteries (LIBs). Experimental parameters such as feeding sequences, reaction time and reaction temperature are discussed and the obtained LMFP are characterized by XRD, SEM and TEM. To understand the formation of LMFP, a reaction mechanism is proposed. The proposed mechanism indicates that the suitable concentration of M Li (M = Fe, Mn) antisite defect can improve the electrochemical performance of the material. The charge–discharge data of obtained LMFP shows that the LiMn 0.9 Fe 0.1 PO 4 material synthesized at 180 °C for 4 h and then sintering with sucrose at 650 °C for 5 h under argon protection has the highest discharge capacity, which is 149.2 mAh g −1 at 0.1C rate.

When lithium-ion batteries age with cycling, the battery capacity decreases and the resistance increases. The aging mechanism of different types of lithium-ion batteries differs. The loss of lithium inventory, loss of active material, and the increase in resistance may result in battery aging. Generally, analysis of the battery aging mechanism requires dismantling of batteries and using methods such as X-ray diffraction and scanning electron microscopy. These methods may permanently damage the battery. Therefore, the methods are inappropriate for the battery management system (BMS) in an electric vehicle. The constant current charging curves while charging the battery could be used to get the incremental capacity and differential voltage curves for identifying the aging mechanism; the battery state-of-health can then be estimated. This method can be potentially used in the BMS for online diagnostic and prognostic services. The genetic algorithm could be used to quantitatively analyze the battery aging offline. And the membership function could be used for onboard aging mechanism identification.

In the first part of this work, we propose dissipative cell equalization (DCE) algorithm based on remaining charging capacity estimation (RCCE) and establish a pack model with 8 cells in series. The results show that RCCE-DCE algorithm is suitable for on-line equalization in electric vehicles (EVs) and no over-equalization happens. However, 1% pack capacity difference from the DCE theoretical pack capacity is observed with RCCE-DCE algorithm. Therefore, as the second part of the series, we propose fuzzy logic (FL) DCE algorithm based on charging cell voltage curves (CCVCs). Cell capacities and SOCs are fuzzily identified in FL-DCE algorithm by comparing cell voltages at the beginning and end of charging. Adaptive FL-DCE is further improved to prevent over-equalization and maintain the equalization capability. The simulation results show that pack capacity difference from the DCE theoretical pack capacity with the adaptive FL-DCE is smaller than that with RCCE-DCE algorithm, and the duration of the infant stage is also shorter. The proposed adaptive FL-DCE is suitable for on-line equalization in EVs and well prevents over-equalization.

Because of the inevitable inconsistency during manufacture and use of battery cells, cell variations in battery packs have significant impacts on battery pack capacities, durability and safety for electric vehicles (EVs). To reduce cell variations and increase pack capacity, cell equalization is essentially required. In the series of two papers, we discover that dissipative cell equalization (DCE) using dissipative resistances is a feasible on-line equalization method for battery packs in EVs. We subsequently propose on-line equalization algorithms for lithium-ion battery packs based on charging cell voltage curves (CCVCs). The objective of these algorithms is to maximize pack capacities by conditioning CCVCs. As the first paper of the series, we first briefly review equalization topologies and algorithms. We discover cell remaining charging capacity (RCC) can be on-line estimated and further propose DCE algorithm based on remaining charging capacity estimation (RCCE). We establish a pack model with 8 cells in series and simulate 4 scenes with different cell variations. RCCE–DCE algorithm is proved to be effective by comparing pack capacities with/without RCCE–DCE algorithm. The equalization capability and over-equalization prevention are further examined, and the result shows that RCCE–DCE algorithm is suitable for on-line equalization in EVs.

A new method, probability density function (PDF), is proposed for evaluating the state of health (SOH) of electric storage batteries by analyzing the charge/discharge (C/D) data. First, a comparison of the PDF method, the cyclic voltammogram (CV), incremental capacity analysis (ICA) and differential voltage analysis (DVA) is provided. The mathematical basis of the four methods are in agreement. Moreover, the PDF method and the ICA/DVA have an equivalence verified by mathematical derivation. Thus the results acquired by the PDF and the ICA/DVA are quite similar. LiFePO 4 and LiMn 2 O 4 batteries are tested to demonstrate the PDF method. Coin cells are tested by the PDF and the CV methods. Results show that the PDF curves and the CV curves have similar shapes. In addition, durability tests are conducted on four commercial batteries to analyze the aging regularities using the PDF method. The PDF results show that the height of the peak reduces as the battery capacity fades. Employing the regularity of peak height reduction with battery aging, an algorithm is proposed to evaluate the SOH online. The PDF method extends the application of the ICA/DVA method. The PDF algorithm is promising to be used in the online SOH evaluation of lithium-ion batteries.

Compared with other commonly used batteries, lithium-ion batteries are featured by high energy density, high power density, long service life and environmental friendliness and thus have found wide application in the area of consumer electronics. However, lithium-ion batteries for vehicles have high capacity and large serial-parallel numbers, which, coupled with such problems as safety, durability, uniformity and cost, imposes limitations on the wide application of lithium-ion batteries in the vehicle. The narrow area in which lithium-ion batteries operate with safety and reliability necessitates the effective control and management of battery management system. This present paper, through the analysis of literature and in combination with our practical experience, gives a brief introduction to the composition of the battery management system (BMS) and its key issues such as battery cell voltage measurement, battery states estimation, battery uniformity and equalization, battery fault diagnosis and so on, in the hope of providing some inspirations to the design and research of the battery management system.