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In this work, the adaptive control of H 2 injector is proposed to reduce the pressure fluctuation in the PEMFC H 2 feeding subsystem, which will reduce the mechanical damage on the membrane. Based on the comparative study on a simplified model, the effectiveness is verified to realizes the self-tuning of the controller’s parameter K p and reduce the pressure fluctuation more than 50% under the situation of changing internal states. On the other hand, limitation of the application is also discussed. This work sheds a light on PEMFC intelligent control to realize higher performance and longer durability in future.

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

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

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

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

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

Optimizing the size of photovoltaic (PV) and energy storage system (ESS) is a key issue to ensure reliable, resilient and economic operation of the microgrid (MG). However, multiplicity of electric vehicle (EV) load profiles and computational complexity of the optimizing models limit the application of existing methods upon restricted scenarios. In this paper, a novel framework combining physical-economical model (PEM) and data-driven model (DDM) is established. First, a data augmentation approach based on reversed Monte Carlo (RMC) is proposed to generate comprehensive synthetic EV load datasets for big data training. Subsequently, a PEM is given to determine the capacity configuration of PV and ESS under each load condition, with the objective to obtain the best financial profitability. The model takes into consideration battery degradation and optimal power dispatch. Finally, a DDM is trained with machine learning techniques to relate optimal sizes with input of EV profiles and MG features. Testing results illustrate that in the test sets, the neural network could predict the optimal configuration within 15% and 20% relative error, respectively, enormously narrowing design space of PEM, and thus produces over 88% runtime savings while maintaining high fidelity and optimality.

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

Promoting Plug-In Hybrid Vehicles (PHEVs) is viewed as a promising strategy to mitigate the energy consumption and greenhouse gas (GHG) emissions in the transportation sector. The actual fuel and electricity energy consumption of PHEVs has recently attracted increasing attention. Distance-based average electricity or fuel consumption is commonly adopted to evaluate the energy efficiency of a vehicle. However, previous studies have predominantly focused on the ratio of electric drive distance to fuel drive distance and did not consider the difference between the electricity generated from onboard engine and offboard grid charging. This study proposes a grid electric driving ratio (grid-EDR) based on the battery energy variation under real road operation conditions for a more accurate calculation of the PHEV electric distance ratio. The method is validated by the actual vehicle driving data of 49 PHEVs in Shanghai and compared with other methods. To evaluate its application, the fuel substitution was calculated using grid-EDR combined with the new European driving cycle fuel consumption calculation method. PHEVs with higher grid-EDRs demonstrate better fuel economy. More than half the PHEVs in this study saved 50–80% of their onboard fuel. By improving the charging frequency, such as constructing more charging infrastructure, grid-EDR can be increased to 0.88 to reduce fuel consumption and greenhouse gas emissions from the transportation sector.

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

Overcharge is one of the most severe safety problems for the large-scale application of lithium-ion batteries, and in-depth understanding of battery overcharge failure mechanism is required to guide the safety design of battery systems. In this paper, the overcharge performance of a commercial pouch lithium-ion battery with Liy(NiCoMn)1/3O2-LiyMn2O4 composite cathode and graphite anode is evaluated under various test conditions, considering the effects of charging current, restraining plate and heat dissipation. Charging current is found to have only minor influences on battery overcharge behaviors, whereas the battery overcharged with pressure relief design (restraining plate and cuts on pouches) and good heat dissipation shows significantly improved overcharge performance and can endure larger amount of overcharge capacity and higher temperature before thermal runaway. Characterizations on cathode and anode materials at different overcharge states are carried out to identify the side reactions inside the battery. The overcharged cathode suffers from electrolyte decomposition, transition metal dissolution and phase transition, but still exhibits no obvious exothermic behaviors before thermal runaway occurs. Severe lithium plating happens on the anode, and would accelerate the overcharge-induced thermal runaway process. Further analysis on the onset temperature of thermal runaway helps to reveal the overcharge-induced thermal runaway mechanism of lithium-ion batteries. The result shows that rupture of the pouch and separator melting are the two key factors for the initiation of thermal runaway during overcharge process.

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

The cause of the thermal runaway problem in lithium-ion batteries problem is still unclear. This bottle neck has prevented increases in the energy density of lithium-ion batteries, of which the technology may stagnate for many years. The diversity of cell chemistries makes this problem more difficult to analyze. This paper reports work conducted by Tsinghua University and its collaborators into the establishment of a thermal analysis database. The database contains comparable data for different kinds of cells using accelerating rate calorimetry and differential scanning calorimetry. Three characteristic temperatures are summarized based on the common features of the cells in the database. In attempting to explain the mechanisms that are responsible for the characteristic temperature phenomena, we have gained new insight into the thermal runaway mechanisms of lithium-ion batteries. The results of specially designed tests show that the major heat source during thermal runaway for cells with Li(NixCoyMnz)O2 cathode and carbon-based anode is the redox reaction between the cathode and anode at high temperature. In contrast to what is commonly thought, internal short circuits are responsible for very little of the total heat generated during thermal runaway, although they contribute to triggering the redox reactions after the separator collapses. The characteristic temperatures provide comparable parameters that are useful in judging the safety of a newly designed battery cell. Moreover, the novel interpretation of the thermal runaway mechanism provide guidance for the safety modelling and design of lithium-ion batteries.

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

Battery electric buses (BEBs) are expected to play a vital role in a green transportation system for China. The Chinese government has dedicated substantial effort and resources to the mass penetration of BEBs, especially in the form of its large amount national fiscal subsidy. Under multiple incentive-based policies, the penetration of BEBs has increased rapidly to make China now rank first among the world's BEB markets. There are a number of unique characteristics in China's BEB industry, including complex product composition, unique technical features, strong policy sensitivity, and effective implementation of government policies. This study focuses on the interaction of these factors among market, technological improvements, and fiscal improvements. The research performed based on an analysis of a large number of actual BEB models and their operational data. More specifically, we analyzed the distribution characteristics of curb mass, top speed, driving power, all-electric range, and energy consumption. Additionally, we examined a technological roadmap for traction batteries and motors of different sizes of BEBs. Finally, the input and output of subsidy benefits associated with fossil oil replacement are examined, which indicate light-duty BEBs exhibit poor performance compared to heavy-duty models. All these results can be used to improve the new incentive polices for BEBs.

Battery electric buses (BEBs) play a very important role of reducing urban tailpipe emissions in transportation sector for China. The national strong incentive policies place China at the forefront of the BEBs' market in the world. However, challenges are even more severe owing to the negative effects of national subsidies on the BEBs industry and technologies’ option. The abovementioned problems indicate that the subsidy mechanism is not scientific and systematic. Therefore, this research focus on the comprehensive evaluating method of addressing the inner influencing principle of technological specification mentioned in new subsidy scheme on the technological development direction of BEBs. The evaluation indicators in the method include cost-benefit and electricity consumption rate and for different size BEBs. The scenario of maximum subsidy obtaining target based on the optimal install battery estimation is set up; then, the potential direction of the BEBs market penetration was investigated. The cost-benefit and energy consumption estimating models are set up to examine the economics of all size BEBs and the difference between BEBs and equivalent conventional bus. Furthermore, the potential positive and negative effects of the technological requirements in the subsidy scheme are investigated The results show that normal-charging BEBs, the market for light-duty BEBs will shrink greatly owing to significant reduction in the subsidy amounts. However, the market penetration of 12 m BEB will increase significantly. LFP batteries will dominate the normal-charging heavy-duty BEB market in near and medium term, but NCM–type traction batteries will gradually be dominating the energy storage market for normal-charging BEBs owing to increasing demands in terms of the energy density of traction batteries. However, the market penetration of fast-charging-type BEBs will growth more rapidly than ever before due to the great increasing in its cost-benefit.

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.

Despite their relatively high capacity, layered lithium transition metal oxides suffer from crystal and interfacial structural instability under aggressive electrochemical and thermal driving forces, leading to rapid performance degradation and severe safety concerns. Here we report a transformative approach using an oxidative chemical vapour deposition technique to build a protective conductive polymer (poly(3,4-ethylenedioxythiophene)) skin on layered oxide cathode materials. The ultraconformal poly(3,4-ethylenedioxythiophene) skin facilitates the transport of lithium ions and electrons, significantly suppresses the undesired layered to spinel/rock-salt phase transformation and the associated oxygen loss, mitigates intergranular and intragranular mechanical cracking, and effectively stabilizes the cathode–electrolyte interface. This approach remarkably enhances the capacity and thermal stability under high-voltage operation. Building a protective skin at both secondary and primary particle levels of layered oxides offers a promising design strategy for Ni-rich cathodes towards high-energy, long-life and safe lithium-ion batteries. Intensive research efforts are underway to enable applications of layered lithium transition metal oxides in batteries. Here the authors report an oxidative chemical vapour deposition technique to conformally coat both the primary and the secondary particles of these oxides to unleash potential applications.

Internal heterogeneity is a significant challenge to the service life of large-format commercial fuel cell stacks. Methods for diagnosing fuel cell stack health, however, are still lacking. This paper proposes a novel diagnostic methodology for analyzing the performance, the consistency between cells and the uniformity within individual cells of fuel cell stacks. Specifically, this three-step methodology can comprehensively analyze the internal heterogeneity of large-format multi-cell stacks. First, a polarization curve test is conducted to provide a quick qualitative performance analysis. Then, the quantitative parameters consistency decline is analyzed by galvanostatic charging and electrochemical impedance spectroscopy methods. Finally, a novel multi-point voltage monitoring method is applied to rule out possible misdiagnoses and to qualitatively analyze cell uniformity. This degradation diagnostic methodology is applied to analyze degradation in several fuel cell stacks, revealing high potential in practice.

The hysteresis of PEM fuel cell output voltage of the single cell and multi-cell stack is presented by experiments, during which the in-situ liquid water visualization at certain point of gas channel, quantitative measurement of the RH (relative humidity) and HFR (high frequency resistance) by EIS (electrochemical impedance spectroscopy) were carried out. It is found that HFR makes little contribution to the hysteresis of voltage. Instead, the hysteresis of liquid water saturation s in GDL is believed to dominant this process. Although the hysteresis of capillary pressure of liquid water in GDL has already been the presented in previous works, it is for the first time that a simplified one-dimensional model on liquid water saturation is updated considering this effect to understand the possible relation between them. Two different liquid water saturation s distributions during imbibition and drainage are obtained by simulation and related hypothesis of different water morphology in GDL is proposed. Furthermore, according to the simulation results, the GDL consisting of material with high resistance to water penetrating is found to have the better performance in the flooding condition, but weak capability to maintain residual water during drainage of PEM fuel cell.

This paper presents a theoretical and experimental analysis of the thermal runaway process of lithium-ion batteries (LIBs) based on the internal combustion engines (ICEs) combustion theory. The experiments used 3.7 V, 31.6 Ah, lithium nickel cobalt manganese oxide cells and an electronically pilot-ignited natural gas engine. The temperature characteristics between the ICE combustion process and the LIB thermal runaway process were analyzed and compared. The process evolution of the LIB failure process was summaried with the ICE working process. One key evaluation parameters (Tsa) and its physical meanings of LIB thermal runaway characteristics was proposed. The reaction mode of the LIB thermal runaway process and its process divisions were analyzed based on the ICE combustion theory. In addition, the method of optimizating the LIB thermal runaway process was pointed out from the viewpoint of reaction mode. The results show that there exist high similarities between the temperature characteristics of the ICE combustion process and the LIB thermal runaway process. The temperatures of the above two processes first rise slowly, then rise sharply, and finally fall rapidly. The LIB failure process can be divided into four processes similar to those of the ICE working process: assemble, abuse, thermal runaway, and eruption. Tsa is the key parameter for early warning and evaluation of thermal runaway. The whole process is dominated by reactivity-controlled self-accelerated chemical reaction (RSCR) mode. The LIB RSCR can be characterized in terms of the induction period and duration, similar to the ICE combustion process. The duration is divided into the slow-reaction, fast-reaction and post-reaction periods. The reaction process is always accompanied by derivative processes, such as gassing, erupting and burning. Based on the ICE combustion theory, it can be concluded that the main reason for the un-controlled LIB thermal runaway process is that the internal cell boundaries are not controlled effectively.

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.

Micro-short-circuit (MSC) is a latent risk in power batteries, which may give rise to thermal runaway and even catastrophic safety hazards. The motivation of this paper is to quantitatively analyze MSC in an initial stage, particularly for lithium-ion batteries. To verify the feasibility of the proposed method, an equivalent MSC experiment is carried out. Based on a cell difference model, the cell state of charge (SOC) differences with the mean SOC for a battery pack are estimated by extended Kalman filter. The evaluated SOC difference can track the actual value well. Furthermore, an MSC diagnostic method is developed by employing recursive least squares filter. The method is demonstrated to examine the short-circuit resistance accurately. The results also show that the proposed method requires low computational load for the SOC difference and short-circuit resistance diagnosis.

Retired cells from electric vehicles could provide considerable economic benefits through secondary uses such as energy storage. However, the screening and regrouping of large-scale cells is facing the problem of low efficiency and low accuracy. To address this problem, two rapid and accurate screening approaches are proposed in this study. Firstly, the characteristics of the series-charging curve of large-scale retired cells are investigated. Then, two novel screening models, namely the neural network model and the piecewise linear fitting model, are thus constructed using the capacity and voltage profiles of a small number of sample cells, and the capacity of a large number of cells can be estimated in batches. Moreover, a device for fast switching between series and parallel is designed to improve efficiency, and a regrouping approach for different second-use applications is introduced. Finally, the proposed approaches are verified by simulations and experiments. The main results are as follow: (1) The piecewise linear fitting model should be used in the small sample case, and the neural network model should be adopted in the large sample case for higher estimation accuracy; (2) The proposed approaches are feasible and effective, and several cases illustrate that the capacity estimation error of the proposed approaches is less than 4%; (3) The screening efficiency of the proposed approaches increases with the increase in the number of cells, and screening results of 5000 cells indicate that the screening efficiency of our proposed approach is at least 5 times higher than that of a traditional approach.

A 2D PEMFC model has been built to simulate the current density distribution and liquid water distribution in cathode. In this simulation, the air mass flow rate, relative humidity of the air and cell voltage are considered as the input parameters into the model, the diagram of current density and liquid water distribution in cathode are shown according to the simulation result and compared to each other. The result indicates that the liquid water distribution has a strong positive relation to the current density, where water drops diffuse mainly by the pressure difference (capillary force) in the porous media (the porous media coefficient ep is very low), where the shear force of the air can hardly drive the water drops due to the high friction force of the porous media. In the thin porous media (the porous media coefficient ep is relatively high), the purge effect of the air flow can be enhanced, decreasing the flooding areas in the cathode. Due to the purge effect of the air flow, the outlet of the cathode is the critical areas, where the flooding is most likely to happen. The steep trough at the current density distribution may mean the local flooding, which blocks the oxygen and weaken the chemical reaction.

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

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

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

The non-uniform internal status in one cell and the inconsistency between cells have significant impact on the performance and service life of commercial fuel cell stack. The identification of this phenomenon with in-situ method is the key technology for product development and optimization. This paper presents the corresponding analysis and a practical in-situ method, i.e., the multipoint voltage-monitoring method, to identify the fuel cell stack uniformity and consistency. Cross plate current and voltage variation in bipolar plate can be therefore observed using the proposed method. In addition, an one-dimensional model has been developed to explain this phenomenon. A variable loading rate experiment has been conducted in a four-cell stack to explore the relationship between consistency and uniformity in multi-cell stack. When the loading rate increases from 20 mA/cm2·s to 40 mA/cm2·s, the current concentrating in the cathode outlet also increases. Current non-uniformity is caused by gas distribution inconsistency between cells, which makes cell1 drier than the other cells. Voltage difference between the two monitors of cell1 is about 32mV, and it can be even larger in extreme conditions, like fuel starvation. It means the health condition estimation of fuel cell stack can be easily misdirected, when monitor is placed on different position. Experimental and simulation results show that the multipoint voltage monitoring method can avoid the ‘voltage cheat’ and therefore achieve the real consistency and uniformity 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.

An experimental study of the module-to-module thermal runaway (TR) propagation in a multi-modular battery pack is presented here. During the experiment a cell in one of the modules is triggered by heating to study both cell-to-cell and module-to-module propagation. In order to understand the mechanism and gain insight into the thermal hazards of a battery pack system, the thermal characteristics of the cells in different modules are analyzed in detail. Although the TR-propagations are all triggered from the side next to the heater, the results indicate that the thermal characteristics of the modules vary in different phases. The upward direction of burning flame and heat flow highlight the importance of design considerations in a multi-modular battery pack.

Cell variations always undermine the efficiency of energy utilization of battery pack. How does the temperature non-uniformity affect the status of cell variations remains unclear. This paper investigates the underlying mechanisms of the status change of cell variations caused by temperature non-uniformity in a battery pack. First, the status of cell variations has been defined using a graphical model, in which each cell has a coordinate of (capacity, electric quantity). Second, the status transformation caused by temperature non-uniformity has been clarified. Dynamics that describes the thermal-electrochemical effect on the movement of the coordinate for individual cells are firstly established by transformation functions. The movement of the cell locations in the graphical model can vividly interpret the evolution of cell variations caused by temperature non-uniformity with the help of the built transformation functions. Modelling analysis points out that 5 °C increase in the maximum temperature difference among the battery pack can lead to 1.5%–2% capacity loss of the battery pack, therefore the capacity loss caused by cell variations due to temperature non-uniformity should be considered in battery system design. The methodology can be further used to analyze the status change of cell variations in a battery pack caused by all kinds of temperature non-uniformities, benefiting the design of battery management system with high efficiency of energy utilization.

The potential of reducing fuel consumption, harmful emission and cost benefit for plug-in electric hybrid buses depended largely on the power management strategy for specific hybrid electric powertrain configuration, especially for those with compound energy storage system. Hybrid energy storage system in this research comprise high energy lithium iron phosphate batteries and super-capacitors, therefore, the key of improving the life cycle cost-benefit is to extend the cycle life for lithium battery. This paper presents an optimal control strategy for the serial-parallel plug-in hybrid electric buses based on the lithium battery degradation model to minimize life cycle operating cost. To derive the globally optimal strategy, an algorithm based on two-dimensional Pontryagin's minimum principle is proposed. With the optimal strategy, the battery degradation is significantly reduced, and the total cost is reduced by 21.7% compared with a plug-in hybrid electric bus with single type energy storage. Further embodies the advantages of hybrid energy storage systems and optimization algorithms.

Partially premixed combustion with low octane fuel aims to reduce NOx and soot emission simultaneously without fuel consumption penalty. Cylinder pressure based combustion phase control is an essential technology for partially premixed combustion. A novel closed-loop combustion phase control strategy for multiple combustion modes is proposed in the current study. The combustion modes are classified into three basic categories based on injection patterns and heat release stages: (1) with only one heat release stage; (2) with two separated heat release stages; (3) with two overlapped heat release stages. Crank angle when 50% fuel is consumed (CA50) is chosen as the combustion phase indicator for the first case. Start of combustion (SOC) of each heat release stage is the combustion phase indicator for the second case. Both SOC and CA50 are the combustion phase indicators for the third case. Each combustion phase is closed-loop controlled by a proportional–integral (PI) controller with the timing adjustments of the corresponding injection. The control strategy is verified under different operating conditions in a 1.9 L light duty diesel engine fueled by gasoline-diesel mixture (volumetric 70% gasoline, 30% diesel). The experimental results show that the control strategy is able to control the combustion phase, reduce cylinder to cylinder variations as well as cycle to cycle variations under the operating conditions with exhaust gas circulation (EGR) rates of 10% and 15%.

Traditionally, only one set voltage monitor was used for fuel cell diagnosis and bipolar plate was regarded as the equipotential body. This study presents a novel analysis about this classic method, and proves that only one-set voltage monitor cannot obtain the real health status of every cell. Experimental results point out that the voltage of each cell along the cell gas channel is variable. The current and voltage redistribution phenomenon, caused by the cell interaction, was observed in the fuel cell stack with graphite bipolar plate. Thus, a one-dimensional model has been proposed to explain the mechanism of the current and voltage redistribution with cell interaction. Simulation result shows that, as long as the abnormal cell exists, the current and voltage distribution of the whole stack is significantly affected. Finally, a multipoint voltage-monitoring method has been developed for fuel cell consistency analysis. New method was applied in a specially designed four-cell stack to validate the model analysis. Additionally, the proposed method was used to diagnose anode starvation, which cannot be easily detected using only the one-set voltage monitor for the anode inlet. All experiments and modeling works prove that the multipoint voltage-monitoring method is effective for fuel cell consistency analysis.

Plug-in electric vehicles (PEVs) have been seen by many countries as a solution to the fossil fuel consumption and urban air pollution problems of the transportation sector. PEV stocks climbed to 1 million in 2015 and doubled as of 2016. Although the global mass market is developing, developmental laws for the technologies and industrialization of PEVs still need to be identified. These laws are necessary for finding solution to the problems of PEV penetration in the future. On the basis of a characteristic analysis of global PEV marketing progress, it can be found that China is becoming the main player in PEV sales and market penetration. It has potential be the largest PEV market in the world in future. Although China has the advantages of market volume, there are still severe challenges for the further mass adoption of PEVs in China. Therefore, a comprehensive evaluation on the evolution of China's PEV industry and technology is desirable as a foundation for identifying key problems regarding market penetration, so as to devise a long-term national PEV strategy. This study are performed based on detailed PEV marketing and technological data to present an in-depth view. A three-dimensional (market penetration rate, constitution, and concentration) evaluation method is proposed, and the market-acceptance indicators and cluster analysis method are used to analyze the correlations between the all-electric range (AER) of PEVs and market acceptance. This research allows us to draw several conclusions: (a) initial PEV market penetration depends mainly on fiscal incentive policies, which also strongly influences the technological roadmap for PECs; (b) A0-and A-class sedans are the dominant models for individuals, and small battery electric cars (BECs) will hold a large market share for a long time; (c) range-extended-type plug-in hybrid electric cars (PHECs) are suitable and competitive for Chinese local companies; and (d) the acceptable cost-benefit AERs are 150–170 km for privately purchased BECs. Electric taxis, however, require the AER to be over 300 km.

A dead-ended anode (DEA) has advantages such as simple structure, high reliability, and low price, and is widely utilized in polymer electrolyte membrane fuel cell (PEMFC) systems. Empirical parameters are commonly adopted in control-oriented models for such systems, and detailed information about mass transport processes is usually not available. Such models are neither helpful for understanding the internal processes within fuel cells, nor for designing control algorithms to improve system performance. A control-oriented model considering the mass transport processes and actuator properties is still lacking. This paper proposes a nonlinear dynamic mechanism model for the DEA system that can describe the dynamic voltage drop during water flooding with a large current density. The properties of the major components are explained in details, and the procedure of how the purging valves affects the mass transport and cell voltage is revealed quantitatively. The relationship between the minimum cell voltage and purging operations is summarized. The results show that (1) the proposed model can capture the stable and dynamic properties of a fuel cell with a DEA, (2) the cell voltage loss during closing of the purging valve is mainly caused by a decrease in oxygen and hydrogen partial pressures on the catalyst layers and an increase in the liquid water saturation ratio in the gas diffusion layers (GDLs); (3) the most important internal states that affect the stack voltage during purging is the liquid water saturation ratio in the GDLs.

Accurate battery energy storage system (BESS) state of health estimation is important to microgrid optimization and energy management. This paper presents a coupled optimization-oriented reduced-order aging model for BESS under microgrid operating conditions. Three dominant physical degradation reactions, namely solid electrolyte interface growth, loss of active material and lithium plating are derived from the single particle model, where the three side reactions are coupled with each other. Five accelerated aging experiments are designed and conducted with various charging-rate and state of charge range to approximate the microgrid conditions. The proposed aging model is calibrated against the experimental data while an excellent fitting result is realized with most of relative errors less than 1%, indicating the accuracy of the reduced-order aging model. The contribution of the three side reactions on total capacity loss is consistent with the incremental capacity analysis, which proves the physical validity of the proposed model.

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

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