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There is an urgent need for high-safety and high-energy lithium-ion batteries to satisfy the rapidly increasing need for energy storage. Nickel-rich layered cathodes have been at the forefront of the revolution for batteries due to their relatively high capacity and low cost. However, with the increase of nickel content, the batteries suffer from severe safety concerns, which caused by thermal runaway. Here we show that the ultraconformal cathode-electrolyte interphase (CEI) protective skin with high inorganic content dramatically enhances the safety of high-energy practical Li-ion pouch cells. We find that the robust CEI skin significantly improves the intrinsic thermal stability, mitigates the evolution of oxygen resulting from phase transition, and effectively suppresses the associated parasitic reactions between the delithiated cathodes and electrolyte. The in-situ CEI engineering strategy is simple and suitable for practical industrial manufacture, and it provides design ideas for aggressive nickel-rich cathodes towards safe and high-energy batteries.

Cascaded thermal runaway (TR) propagation is the utmost safety issue for large-format lithium-ion battery (LIB) modules because of the high risk of system fires or explosions. However, quenching TR without side effects still remains a challenge. Herein, we delivered an ultrathin smart firewall concept for avoiding the TR propagation in a LIB module. We demonstrate that the firewalls have thermally-triggered switchable thermal physical properties because of the synergistic effect of non-flammable phase change materials (NFPCM) and flexible silica nanofiber mats. Under TR condition, the firewalls give rise to multiple functions simultaneously, including cooling, fire extinguishing and thermal insulation. Consequently, the TR propagation between fully charged 50 Ah LIBs, with a transient thermal shock of up to 53 kW, is successfully suppressed by 1-mm-thick smart firewalls, yielding a maximum cell-to-cell temperature gap of 512 °C. The smart firewall design provides a reliable approach to quench TR propagation in large-format LIBs, which can also be suitable for other dynamically adaptive thermal-protection applications for oil tanks, space exploration, and firefighting equipment.

Accompanied by the adoption of aggressive cathodes to continuously improve batteries energy density, enhancing their safety is becoming increasingly urgent for the electric vehicle development. In-situ controllable formation of robust cathode-electrolyte interphase (CEI) with high inorganic content seems to be the most promising strategy to address the thermal runaway concerns. Moreover, the in-situ formation strategy via suitable electrolyte replacement or electrolyte additives is extremely simple yet effective, especially for industrial manufacture of batteries. Here, this paper briefly reviews recent advanced CEIs formed by conventional carbonate-based electrolytes, fluorinated electrolytes, concentrated electrolytes, and solid state electrolytes. The focus on the thermal stability of the cathodes after CEI modification, and at the same time conduct safety tests at the material, cell, and module levels for comprehensive evaluation are encouraged. The review will provide inspiration for future developments in battery safety and push forward the practical applications of newly developed high-energy density batteries.

Safety concerns are the main obstacle to large-scale application of lithium-ion batteries (LIBs), and thus, improving the safety of LIBs is receiving global attention. Within battery systems, the internal short circuit (ISC) is considered to be a severe hazard, as it may result in catastrophic safety failures, such as thermal runaway. Considering this, we provide a comprehensive review on the mechanism and evolutionary process of ISC, including modeling and simulation experiments and the methods of detection and diagnosis. First, ISC types and the inducing mechanism under various inducements are analyzed, and the evolution process of ISC is divided into three stages according to electrical and thermal characteristics. Second, eleven existing ISC substitute experimental methods are listed in detail, and three coupling models of electric-thermal-ISC models are introduced to simulate the characteristics of ISC. Third, existing ISC detection methods are reviewed in detail, divided into six categories. Moreover, we propose methods for ISC detection under four special conditions: ISC detection for the cells before grouping, ISC detection method during electric vehicle dormancy, ISC detection based on equilibrium electric quantity compensation to address negative impact of the equalization function of the battery management system on ISC detection, and effective fault tree diagnosis for the joint identification and early warning of multiple battery faults. Fourth, existing ISC prevention methods are reviewed. Finally, the key technologies of ISC are prospected, revealing that the development of big data and artificial intelligence technology will promote the accuracy and timeliness of ISC detection.

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

The particulate matter released by lithium-ion batteries during eruption and combustion caused by thermal runaway may contain heavy metal pollution elements. The aim of this study is to reveal four heavy metal element content and size distribution of particulate matter released by abused prismatic Ni-rich automotive lithium-ion batteries. A fully charged commercial 50 Ah Li(Ni0.6Mn0.2Co0.2)O2 cell was triggered by heating in a sealed chamber at nitrogen environment. The particulate matter with size being below 500 μm were divided into three samples using sieves. Four soil pollution elements, including nickel, copper, zinc, chromium, were detected, and sizes were analyzed. The results show that all these four elements are found, and nickel and copper are found in all samples. Among the four elements, Ni had the largest mass percentage, followed by Cu, Zn and Cr. These particulate matter account for 1.7% of the cell mass, with the minimum size and median size being approximately 1.45 μm and 162.9 um. The element content reveals the pollution from LIBs and the size distribution provides a basis for the design of the particle filter pore.

As the top two plug-in electric vehicle (PEV) markets in the world, China and the United States of America (USA) have developed different market structures that are influenced by government policies, test procedures, customer acceptance, and vehicle performance. There are differences in PEV test procedures and vehicle class definitions as well. This paper quantifies such differences and compares PEV characteristics and markets in the two countries using actual data collected over several years. First, although China surpasses the USA in annual PEV production because of generous PEV policies and higher charging infrastructure availability, the USA has a higher PEV adoption rate per capita (2.3 versus 0.81 per 1000 people at the end of 2017). Second, the most popular vehicle classes are A00 battery electric vehicles (BEVs) in China but long-range mid-size BEVs in the USA. Moreover, China’s electric and plug-in hybrid sport utility vehicle market is growing quickly. Third, the electricity consumption rated under Chinese test procedures is 20%–40% lower than that rated under US procedures. The sales-weighted electricity consumption of an average BEV in China is 23% lower than that in the USA because of larger proportions of small and micro BEVs in China. Fourth, PEV battery cost in the two countries is currently close to $210–$220 kWh. Finally, China has higher numbers of charging infrastructure, and the ratio of PEVs to public chargers is 9.0 in China versus 17.9 in the USA at the end of 2018.

Self-humidification with anode/cathode recirculation was found to be an alternative in the place of external humidifier according to the previous studies in the last decade, however, there is still a lack of quantitative experimental and theoretical comparison between them. In this study, the dynamic PEM fuel cell system model with anodic and cathodic exhaust gas recirculation is proposed and related model calibration and verification are carried out by measured output voltage, gaseous pressure and mole concentration during the dynamic process. The model simulation shows quite good agreement with the experimental result and source of error is discussed. Based on developed model, it is simulated that the humidification effect is much better for cathode recirculation compared with anode recirculation, resulting in performance improvement, especially under the condition of low inlet RH. However, this improved humidification effect is weakened, considering the structure limit of recirculation pump and gas channel in this case. Besides, the power consumption of cathode recirculation pump is found to be much higher than that of anode recirculation. As a result, the anode recirculation is found to be a better solution considering the energy efficiency and mechanical structure limit. In contrast, cathode recirculation conducts better humidification effect, which is more suitable for the short-time low-inlet-humidity working conditions.

Lithium-ion (Li-ion) cells degrade after repeated cycling and the cell capacity fades while its resistance increases. Degradation of Li-ion cells is caused by a variety of physical and chemical mechanisms and it is strongly influenced by factors including the electrode materials used, the working conditions and the battery temperature. At present, charging voltage curve analysis methods are widely used in studies of battery characteristics and the constant current charging voltage curves can be used to analyze battery aging mechanisms and estimate a battery’s state of health (SOH) via methods such as incremental capacity (IC) analysis. In this paper, a method to fit and analyze the charging voltage curve based on a neural network is proposed and is compared to the existing point counting method and the polynomial curve fitting method. The neuron parameters of the trained neural network model are used to analyze the battery capacity relative to the phase change reactions that occur inside the batteries. This method is suitable for different types of batteries and could be used in battery management systems for online battery modeling, analysis and diagnosis.

Fuel cell has a broad application prospect, whereas the durability, cost and performance need optimization for further commercialization. Fuel cell activation procedure is an important way to effectively improve fuel cell performance, but has received little attention, especially during automotive operation. In this paper, we focus on the performance characteristics of the on-board fuel cell activation stage and introduce two online performance analysis methods suitable for vehicle conditions. It is found that during the activation phase, the voltage degradation caused by vehicle operating conditions and the performance optimization introduced by hydration occur simultaneously. Due to the effect of operating current on hydration, fuel cells exhibit different performance characteristics at different currents. The analysis provides a basis for the control strategy design in the activation phase of automotive PEMFC, so that the fuel cell can quickly achieve the best performance.

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.

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.

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.

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.

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.

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.

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.

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.

Being a promising alternative to internal combustion engines, the power output of a fuel cell stack is strongly dependent on its operating conditions. The fuel cell stack is integrated with auxiliary components to form a system that ensures optimal performance at the current layout. Here, we evaluated the key requirements of different components, designed and integrated an 80kW fuel cell system for automotive applications. The fuel cell system performance, with emphasis on the control accuracy of hydrogen and air sub-systems, is demonstrated. The system has peak power of 89.6 kW and peak energy efficiency of 57%. For the air subsystem, the air pressure and flow rate are controlled, both of which could achieve

The fuel cell hybrid electric vehicle (FCHEV) is a new type of vehicle with the advantages of high efficiency and environmental protection. As the government and society pay more and more attention to environmental and energy issues, the development of FCHEV has entered an important stage. The control algorithm of FCHEV is a key technology of new energy vehicles and requires research. This research mainly focuses on the power system modeling and the longitudinal dynamics control and simulation of FCHEV. Based on the tire model, a new slip ratio estimation strategy was proposed. The target drive torque control algorithm and the anti-slip control algorithm adopt the feedforward control and Proportional-integral feedback control. The hydraulic braking force and the regenerative braking force were distributed to ensure that the motor exerts the maximum regenerative braking capability, while the braking force distribution meets the requirements of the ECE braking regulations. On the MATLAB/Simulink software platform, a FCHEV power system model and a coordinated driving and braking control model were established. Through simulations in different working conditions, this paper proved the performance of the new slip ratio estimation algorithm and the feasibility of the dynamics control algorithm.

Summary Heating battery at low temperatures is fundamental to avoiding the range anxiety and the time-consuming charging associated with electric vehicles (EVs). One method for achieving fast and uniform battery heating is to polarize the cell under pulse currents. However, the on-board implementation of this method leads to an increase in the cost and size. Therefore, in this study, an adapted EV circuitry compatible with the existing one and an optimized operating condition are proposed to enable rapid battery heating. With this circuit, electricity transfer between the cells can be realized through a motor, leading to remarkably higher battery currents than those of the conventional circuit. The increase in the maximum heating currents (from 1.41C to 4C) resulted in a battery temperature rise of 8.6°C/min at low temperatures. This heating method exhibits low cost, high efficiency, and negligible effects on battery degradation, practical and promising on battery heating of EVs., Graphical abstract, Highlights • Rapid temperature rise, high efficiency, and low cost for battery heating are obtained • The batteries are divided into two modules and connected to the inverter individually • The electricity transferred between battery modules is motivated by a static motor • The pulse currents for battery heating are improved significantly at any frequency, Electrochemistry; Electrochemical Energy Storage; Energy Engineering; Energy Systems; Energy Materials

Sub-zero temperature causes performance degradation, lifespan shortage, and even some safety issues of Li-ion battery cells, such as the internal short circuit. Preheating has become a critical issue for electric vehicle (EV) promotion in the high-latitude area or cold temperatures. To address this issue, a double-direction liquid heating-based Cell-to-Chassis (CTC) battery module is proposed for an extreme low-temperature environment (-40°C). Two cooling plates and the battery module are embedded in the chassis to reduce the component number. Besides, the volume energy density gets increased by 26.3%. The numerical calculation indicates that the proposed system is more efficient than the commonly utilized battery thermal management system (BTMS) in EVs. Moreover, the preheating effect with different heating intervals is compared; eight-minutes-preheating is proved to be more effective in preheating the battery module to 0°C with less energy consumption. Furthermore, a multi-objective optimization design is further carried out considering the heating rate, thermal safety, thermal uniformity, and energy cost. The impact of the mass flow rate in the mini-channels and the PTC heating film power are analysed through sensitivity analysis. Finally, the optimal design scheme is selected. The minimum, maximum, and volume average temperatures of the battery module are 273.2K, 296.7K, and 286.9K, respectively. Moreover, the temperature standard deviation is further reduced to 8.8K without much energy cost increment. This study guides for combing the efficient BTMS with the EV chassis for all-climate applications, especially with integrated preheating/cooling functions, which is feasible for the fast charging and cold environment applications, both the thermal management efficiency and the volume energy density can be effectively enhanced.

This paper presents a novel dual-layer framework to find the most economic photovoltaic and battery capacity combination of a grid-connected DC microgrid. The system cost during the whole lifetime is addressed in the optimization framework through the net present cost based objective function, where the investment cost, battery replacement cost and operating and management cost are taken into account. The proposed CAPN model, which combines all of the battery ageing stressing factors, is incorporated to estimate the replacement number of the battery. A particle swarm optimization algorithm, which is initialized with a rule-based power dispatch strategy, is put forward in the energy management level to achieve the optimal power dispatch. In order to adapt to variant operating conditions, the ambient and electric vehicle charging profiles throughout a whole year are employed as the input conditions of the optimization problem. A 61.8k RMB reduction is realized with the optimal configuration under the proposed method compared with the rule-based energy management strategy, indicating the superior of the proposed optimization methodology. Besides, the optimal power dispatch is also implemented with the extra power fed back to the utility grid.

In this paper, we propose a multi-objective optimization problem of power components parameters for a pre-defined driving cycle and energy management algorithm regarding fuel economy, system durability, power components cost and battery volume. We eliminate the parameter vectors that can’t meet the vehicle power demand. Then applying the algorithm to the model of a fuel cell city bus gives the opportunity to choose the best values for battery capacity Q bat and the maximal net output power of the FCS P fcsmax according to the contour diagram. Simulation results show that for a "New Europe Driving Cycle", a battery capacity of 96 Ah and an FCS maximal net output power of 100 kW are optimal for the fuel economy, system durability, power components cost and battery volume limitation of a fuel cell city bus.

Internal short circuit (ISCr) is one of the major obstacles to the improvement of the battery safety. The ISCr may lead to the battery thermal runaway and is hard to be detected in the early stage. In this work, a new ISCr detection method based on the symmetrical loop circuit topology (SLCT) is introduced. The SLCT ensures that every battery has the same priority in the circuit and every battery will contribute the same amount of short-circuit current to the ISCr once the ISCr happens. The ISCr battery could be identified by the combination of the ratio of the short-circuit currents and the sign of the short-circuit currents. The recursive least square method is adopted for the real-time application and the optimized ammeters allocation is derived from the mathematic deduction. The battery pack based on the individual DP (dual polarization) battery model is established to verify the ISCr detection method. The 1–1000 Ω s ISCr (the early stage ISCr) can be effectively detected within 1–125 s. The SLCT provides the possibility of new battery pack designs and new battery management methods. The proposed ISCr detection method shows excellent effectiveness and efficiency on the identification of the ISCr battery in the early stage.

Partially premixed combustion aims to reduce NOx and particulate matter emission without fuel consumption penalty. Low temperature reactions are important for the partially premixed combustion. In the current study, a self-adaptive strategy is introduced to predict the motoring pressure by means of an equivalent isentropic index. The start of the low temperature reactions is detected when the pressure difference between the measured pressure and the predicted pressure is higher than a threshold value. Experimental results show that the maximum variation between the measured and the predicted motoring pressure before the top dead center is less than 0.02 bar. It is also demonstrated that the presented detection method is able to detect the start of the low temperature reactions under different engine operating conditions with varying injection timing, injection duration, number of injections and exhaust gas recirculation rate.

Durability and reliability are still major challenges of vehicular polymer electrolyte membrane fuel cell (PEMFC) systems. With exhaust gas recirculation on both the anode and cathode sides, two important functions can be achieved: the voltage clamping in low current density, and the self-humidification without any external humidifiers. The former restrains catalyst decay in small load working conditions, and the latter is beneficial for improving the cold-start ability. In this study, dynamic performances and stable characteristics of a fuel cell system with dual exhaust gas recirculation are firstly experimentally studied using an orthogonal test method. System parameters, including humidification temperature of cathode external humidifier, fresh air stoichiometric ratio (SR), current density, cathode and anode recirculation pump speeds, are regarded as key factors in the experiments based on the testing conditions of the test-bench. Two four-factor and three-level orthogonal tables are designed, and the effects of key factors on system performance indices (average cell voltage, relative humidity (RH) at cathode inlet, high frequency resistance (HFR), oxygen concentrations at cathode inlet and outlet, and the concentration difference between these two positions) are investigated. Results show that: (1) with the cathode recirculation, the cell voltage can be reduced in low current densities by coordinately adjusting the recycled gas flow and reducing fresh air SR; (2) with the dual recirculation, the fuel cell membrane can be well hydrated, and system performance only shows 3% reduction compared with a system with an external humidifier; (3) the difference between the oxygen molar concentration at the inlet and outlet of cathode gas channels becomes small using dual recirculation.

The Pontryagin's minimum principle is utilized in this paper to determine the best solution of component sizing and energy management strategy for a plug-in hybrid electric vehicle which is equipped with a hybrid energy storage system. The hybrid energy storage system, including batteries and supercapacitors, is an effective solution to extend battery life span and reduce the vehicle operating cost. The operating costs of different hybrid energy storage system candidates, including fuel cost, electricity cost, and battery degradation cost over 6 consecutive China bus driving cycles, are minimized by using a 2-dimensional Pontryagin's minimum principle algorithm proposed in this paper. The proposed Pontryagin's minimum principle algorithm not only determines the optimal energy management strategy, but also globally finds the optimal battery and supercapacitor sizes. It is shown that the operating cost strictly decreases with increasing battery and supercapacitor sizes. In addition, simulation results show that the operating cost is reduced by up to 28.6% when compared to a conventional hybrid powertrain without supercapacitors. Thus the effectiveness of adopting supercapacitors in plug-in hybrid electric vehicles is verified.

Electric buses face problems of short driving range, slow charging, and high cost. To improve the performance of electric buses, a novel hybrid battery system (HBS) configuration consisting of lithium iron phosphate (LFP) batteries and Li-ion batteries with a Li4Ti5O12 (LTO) material anode is proposed. The configuration and control of the HBS are first studied, and a LFP battery degradation model is built. Simulation result indicates that the HBS can help us to mitigate LFP battery degradation. Then, the HBS is optimally sized for electric buses to achieve minimum cost. The daily bus operation and charging patterns as well as LFP battery degradation are considered. The optimal HBS has 10.7% and 19.3% lower total cost than the single LTO-battery and LFP-battery configurations, and has higher range flexibility than the single LTO-battery configuration.

This paper proposes a co-estimation scheme of state of charge (SOC), state of health (SOH), and state of function (SOF) for lithium-ion batteries in electric vehicles. The co-estimation denotes that the SOC, SOH, and SOF are estimated simultaneously in real-time application. The model-based SOC estimation is fulfilled by the extended Kalman filter. The battery parameters related with the battery SOH and SOF are online identified using the recursive least square algorithm with a forgetting factor. The capacity and the maximum available output power are then estimated based on the identified parameters. The online update of the capacity and correlated parameters help improve the accuracy of the state estimation but with limited increase in the computation load, by making good use of the correlations among the states. The co-estimation scheme is validated in a real battery management system with good real-time performance and convincible estimation accuracy.

The safety concern is the main obstacle that hinders the large-scale applications of lithium ion batteries in electric vehicles. With continuous improvement of lithium ion batteries in energy density, enhancing their safety is becoming increasingly urgent for the electric vehicle development. Thermal runaway is the key scientific problem in battery safety research. Therefore, this paper provides a comprehensive review on the thermal runaway mechanism of the commercial lithium ion battery for electric vehicles. Learning from typical accidents, the abuse conditions that may lead to thermal runaway have been summarized. The abuse conditions include mechanical abuse, electrical abuse, and thermal abuse. Internal short circuit is the most common feature for all the abuse conditions. The thermal runaway follows a mechanism of chain reactions, during which the decomposition reaction of the battery component materials occurs one after another. A novel energy release diagram, which can quantify the reaction kinetics for all the battery component materials, is proposed to interpret the mechanisms of the chain reactions during thermal runaway. The relationship between the internal short circuit and the thermal runaway is further clarified using the energy release diagram with two cases. Finally, a three-level protection concept is proposed to help reduce the thermal runaway hazard. The three-level protection can be fulfilled by providing passive defense and early warning before the occurrence of thermal runaway, by enhancing the intrinsic thermal stability of the materials, and by reducing the secondary hazard like thermal runaway propagation.

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

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

Promoting Plug-in Hybrid Electric Vehicles (PHEVs) is a promising strategy to mitigate the climate change and improve the environment in the road transportation sector. To evaluate the complex energy saving and GHG emission reduction contribution of on-board fuel and electricity, utility factor (UF) is the most widely adopted parameter to weight the distance driven by electricity and on-board fuel, respectively. However, few researches focus on city-level actual utility factors based on quantitative analysis of the influencing factors, especially in China. In this research, a probability-distribution-based PHEV UF function is constructed from travel pattern parameters, charging frequency and PHEV charge depleting range. Then the urban city-level UF at actual travel and charging patterns are derived from PHEV driving big data in seven typical cities in China. The sales-weighted actual UF is 0.49 and 0.64 for a 50 km-electric-range PHEV and a 80 km-electric-range PHEV; therefore, PHEVs with over 80 km electric range are recommended in China urban areas due to better energy saving and emission reduction potential. Finally, carbon and pollutant emissions are analyzed, actual PHEV carbon emission is 133–137 g/km currently while it could drop to 71–88 g/km at a grid with 80% renewable energy; therefore, cleaner grid would largely contribute to PHEV environmental effects.

Electric vehicles (EVs) occasionally experience accidents caused by the thermal runaway propagation (TRP) of Li-ion batteries. Countermeasures for TRP through battery thermal management systems have typically been conducted by enhancing heat dissipation or thermal insulation individually, without considering their coupled effects. In this study, the synergy of heat dissipation underneath the battery module with thermal insulation between adjacent cells was investigated through experiments and simulations for TRP elimination. Simulations of the heat flux were conducted based on a 3D model, and the results agreed well with the failure behavior in the experiments. The results indicate that pure liquid cooling fails to mitigate the TRP of a prismatic battery module because the heat flux between a thermal runaway cell and its neighbor is difficult to attenuate by cooling plate placed underneath, which only reduces it from 885.7 to 848.2 W. Furthermore, the insulation provides more time for the heat to be drained through the cooling plate; therefore, successful TRP inhibition requires the cooperation of thermal insulation and liquid cooling. Six critical conditions under which no TRP occurs and a theoretical diagram regarding the nexus of heat dissipation and thermal insulation were obtained from the modeling analysis. Finally, a universal criterion was proposed to optimize the design of thermal insulation and heat dissipation in a battery module, providing insight into the thermal safety design of EVs.

It is an essential issue that fast charging of lithium ion battery which is restricted by lithium deposition. The aim of this research is to provide an optimal charge current of lithium ion battery, by which the theoretically fastest charging speed without lithium deposition is able to be reached. In other words, a maximal acceptable charge current of lithium ion battery is proposed. The expression of the charge curve is derived mathematically according to the lithium deposition criterion and validated by an electrochemical model. The charge current is found highly related with the anode equilibrium potential Un, as well as the charge transfer resistance Rct which is a critical parameter. Lower Rct can accommodate higher acceptable charge current amplitude, indicating that better charge capability of the battery. For real applications, the charge current can be easily derived from this method and directly used to charge the lithium ion battery in electric vehicles.

Recently with the large-scale access of renewable energy into power system through power electronics, distributed energy systems attract more attention. However, low inertia decreases the voltage or frequency stability and anti-disturbance capability of systems, causing power quality is vulnerable to the intermittency and uncertainty in photovoltaics or wind plants. Therefore, the virtual inertia control (VIC) is proposed to maintain system stability. This paper proposes a virtual adaptive inertia control (VAIC) strategy. The states of energy storage battery packs (ESBPs) are estimated online by the dual extended Kalman filter. Then the virtual inertia and droop parameters are designed through the fuzzy logic and virtual battery algorithms based on battery states and bus voltage fluctuations, aiming at distributing inertia and power in the dynamic and steady periods respectively. The stability is analyzed using the small signal model, and its feasibility is verified on the Matlab/Simulink platform. In microgrids with multiple ESBPs, the VAIC distributes power and inertia among ESBPs considering their different capabilities of power supplying and the system requirement for inertia. It can suppress the voltage fluctuations, improve the system stability, and achieve the decentralized and coordinated control during the whole running process of microgrid.

With the progress of renewable energy technologies, distributed energy system (DES) has become attractive due to its flexibility and interaction with power systems. Battery energy storage system (BESS) is an indispensable part of DESs, the control strategies of which have a great influence on system performance. In this paper, we present a novel adaptive droop control (ADC) for energy storage batteries. The state and model parameters of energy storage batteries are estimated simultaneously online by dual extended Kalman filter (DEKF) algorithm. Then the droop controller is designed based on the battery parameters by the “virtual battery” algorithm, benefit from which power can be distributed among battery packs adaptively for their power characteristics, meanwhile SOC balancing can be achieved automatically. The proposed control strategy is validated in MATLAB/Simulink environment with a direct-current (DC) microgrid model where battery packs are connected to the DC bus through dual active bridge (DAB) converters. Compared with conventional droop controls, the proposed ADC method considers the battery characteristics and battery aging, and it is more suitable for DES with multiple sets of batteries.

With the rapid development of electric vehicles, the safe and environmentally friendly disposal of retired lithium batteries (LIBs) is becoming a serious issue. Echelon utilization of the retired LIBs is a promising scheme because of its considerable potential for generating economic and environmental value. The most outstanding technical challenge of echelon utilization is how to sort and regroup the large-scale retired LIBs efficiently and accurately. In this paper, the status and challenges of echelon utilization for the retired LIBs are reviewed. First, the criteria, policies, regulations, markets, costs, and values of echelon utilization are summarized comprehensively to illustrate its potential and expose existing problems and pain points. Second, the key technologies related to large-scale echelon utilization of LIBs are detailed; valuable opinions and technical routes are presented for the selection and rapid estimation of sorting indices, the classification and regrouping algorithm, evaluation of the sorting results, and other aspects. In particular, a multilevel and multidimensional fast sorting method is proposed for large-scale echelon utilization of retired LIBs that considers different scenario constraints. Valuable solutions to the key technical problems are given, such as predicting the characteristics of retired LIBs with in-service data and building a fast sorting model from a small number of samples to sort large quantities of LIBs. Finally, the technological prospects of echelon utilization are discussed. Big data and artificial intelligence can be used to promote further development and application of echelon utilization, which may eventually be applied to managing the whole life cycle of LIBs.

In order to fulfill the energy and power demand of battery electric vehicles, a hybrid battery system with a high-energy and a high-power battery pack can be implemented as the energy source. This paper explores a cloud-based multi-objective energy management strategy for the hybrid architecture with a deep deterministic policy gradient, which increases the electrical and thermal safety, and meanwhile minimizes the system’s energy loss and aging cost. In order to simulate the electro-thermal dynamics and aging behaviors of the batteries, models are built for both high-energy and high-power cells based on the characterization and aging tests. A cloud-based training approach is proposed for energy management with real-world vehicle data collected from various road conditions. Results show the improvement of electrical and thermal safety, as well as the reduction of energy loss and aging cost of the whole system with the proposed strategy based on the collected real-world driving data. Furthermore, processor-in-the-loop tests verify that the proposed strategy can achieve a much higher convergence rate and a better performance in terms of the minimization of both energy loss and aging cost compared with state-of-the-art learning-based strategies.

The development of electric vehicle batteries has resulted in high energy density battery pack. Cell-to-Pack (CTP) omits the cell module assembly, can reduce battery pack parts by 40%, improve the battery pack volume utilization rate by 15%–20%. However, the thermal characteristics of CTP under full operating conditions has yet to be verified, the CTP temperature calibration cycle under full working condition is time-consuming, and little analysis has been conducted on CTP internal temperature differentials and heat flow. The present study aims to thermal characteristics of CTP. Results show that CTP temperature calibration based on a thermal resistance grid can realize internal temperature reconstruction, optimum temperature arrangement, and shorten the calibration period by more than 60%. In addition, higher ambient temperatures can increase the maximum temperature of the jelly rolls in CTP cells, while lower ambient temperature can lead to larger differences in jelly roll temperature within a cell; and high-speed driving and fast charging can lead to the highest temperature increase rates under different working conditions up to 0.05 °C s−1.70% of the heat generated during cell discharge is used for self-heating, and the heat dissipation of liquid cooled plate is the main heat dissipation channel.

The double-sided LCC compensation network shows high performances in electric vehicle wireless power transfer (EV WPT) applications owing to its constant output current, low voltage stress and other good characteristics. In order to achieve full power and high-efficiency energy transmission within the specified range of coupling and battery voltage, a controlled rectification scheme became a trend in EVs manufacturers and standards. However, a current oscillation problem emerges in a double-sided LCC system when employing a circulating current control method for the controlled rectifier. This paper proposes a mechanism to understand the oscillation phenomenon caused by the inter-harmonics and a positive feedback scheme. The amplification effect of circuit admittance is believed to be the key cause of the inter-harmonics. Therefore, an oscillation improvement is proposed in this paper by adding an inductor and a capacitor to the secondary LCC network, minimizing the admittance amplification effect. A comprehensive simulation and experimental validations under different rectifier duty cycles, coupling positions and load resistances, confirmed the effectiveness of the proposed method. Interestingly, the optimized system expanded the safe operating region and reduced the oscillating operating points from 18 to 8 out of the 27 possible conditions.