Search

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.

Background: Colic is a common condition in infants Bifidobacterium longum CECT7894 (KABP042) and Pediococcus pentosaceus CECT8330 (KABP041) mix (1 × 109 colony forming units) would improve the symptoms of infantile colic.Methods: A total of 112 exclusively breastfed or mixed fed infants aged n = 48) or placebo (placebo group, PG, n = 42) daily for 21 days.Results: Infants in the IG had significantly shorter crying time (p < 0.001) on day 7 [IG vs. PG, median (25−75th percentile): 38 (3.5–40.5) vs. 62 (40–108) min/day], day 14 [IG vs. PG: 20 (0–40) vs. 50 (30–75) min/day], and day 21 [IG vs. PG: 14 (0–33) vs. 40 (28–62) min/day]. Higher responder ratio and fewer crying/fussing episodes on days 7, 14, and 21 and better stool consistency on day 21 were observed in the IG (p < 0.01) as compared to the PG. Conversely, no significant effects on stool frequency or quality of life were observed.Conclusions: In summary, daily oral administration of B. longum CECT7894 (KABP042) and P. pentosaceus CECT8330 (KABP041) was an effective treatment for shortening crying time due to infantile colic and for improving fecal consistency. This trial was registered retrospectively in December 2019 with a trial number of ISRCTN92431452.

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.

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.

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.

In this paper, the advantages and disadvantages of compressed gaseous hydrogen system and liquid hydrogen system are compared and analyzed from the perspective of hydrogen storage density and energy consumption. It is proved that on-board liquid hydrogen system is the most economical and efficient and environmentally friendly choice for heavy-duty trucks which are running continuously at high speed and long distance. This paper also conducted a joint test of a large-capacity liquid hydrogen system and a fuel cell system. The results show that the on-board liquid hydrogen system can provide relatively stable pressure for the fuel cell stack at dynamic and steady-state conditions. The liquid hydrogen system is highly compatible with fuel cell system and can be applied to high-power fuel cell system. This study makes it promising to develop a new generation of heavy-duty fuel cell truck with long driving range, high load capacity and high power.

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

Background: In December, 2019, a novel coronavirus pneumonia (NCP) was first reported in Wuhan, China NCoutbreak spread rapidly nationwide and grobally, Subs

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.

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.

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.

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.

This paper analyzes the influence of different driving cycles on the integrated optimization of hybrid energy storage system, including the optimization of supercapacitor size and energy management strategy for the electric vehicle application. The driving cycle is divided into micro-trips, and a fuzzy pattern recognition algorithm is proposed to distinguish different micro-trips within a driving cycle. The intensity factor indicates how intensely the micro-trip drains energy from the hybrid energy storage system. The distribution of each driving cycle is analyzed by the probability density function. The integrated optimization of the hybrid energy storage system is conducted based on four driving cycles. Simulation results show that for different driving cycles, the optimal supercapacitor size and the on-line energy management strategy are directly determined by the maximum intensity factor. The driving cycles with similar maximum intensity factors can use same amount of supercapacitor modules and employ the same on-line energy management strategy. Therefore, the optimization results can be easily generalized to practical bus lines.

Durability and start-up ability in sub-zero environment are two technical bottlenecks of vehicular polymer electrolyte membrane (PEM) fuel cell systems. With exhaust gas recirculation on the anode and cathode side, the cell voltage at low current density can be reduced, and the membrane can be humidified without external humidifier. They may be helpful to prolong the working lifetime and to promote the start-up ability. This paper presents an experimental study on a PEM fuel cell system with anodic and cathodic recirculation. The system is built up based on a 10 kW fuel cell stack, which consists of 50 cells and has an active area of 261 cm 2 . A cathodic recirculation pump and a hydrogen recirculation pump are utilized on the cathode and anode side, respectively. Key parameters, e.g., stack current, stack voltage, cell voltage, air flow, relative humidity on the cathode side, oxygen concentration at the inlet and outlet of the cathode side, are measured. Results show that: 1) with a cathodic recirculation the system gets good self-humidification effect, which is similar to that with an external humidifier; 2) with a cathodic recirculation and a reduction of fresh air flux, the cell voltage can be obviously reduced; 3) with an anodic recirculation the cell voltage can also be reduced due to a reduction in the hydrogen partial pressure, the relative humidity on the cathode side is a little smaller than the case with only cathode recirculation. It indicates that, for our stack the cathodic recirculation is effective to clamp cell voltage at low current density, and a self-humidification system is possible with cathodic recirculation. Further study will focus on the dynamic model and control of the dual recirculation fuel cell system.

One of the key battery performances which can be described as the resistance characteristic is the cell voltage response with dynamic currents, and it requires synchronous cell voltages and current. But due to inevitable network latency in modular battery management system (BMS) with controller area network (CAN), cell voltages and current are usually asynchronous. We firstly analyze the sampling and the storage process of battery signals to study the asynchronous mechanism in BMS. We develop an on-line synchronization method using a “global clock” from the master controller to decrease the time delay as much as possible. And we further propose a model based sync method based on the frequency division equivalent circuit model (FDECM) for the battery pack. The low frequency cell difference model is used to identify cell “resistances difference”, and then the optimal time compensation for cell voltages is obtained when the minimum mean absolute derivative (MAD) value of identified resistance differences is reached according to the low frequency characteristic of cell “resistances difference”. The proposed methods are verified by simulation and experiment. The current and cell voltages in the data logger of BMS can be synchronized when the optimal compensation time is applied respectively for each cell. The data after synchronization can meet the requirements of further data analysis and processing, which is of great significance to enhance and improve the control strategy of BMS.

In this paper, the control strategy of a fully-active hybrid energy storage system, which uses two bi-directional DC/DC converters to decouple supercapacitor and battery pack from the DC bus, is proposed based on a 5th-order averaged model. Three control objectives, the battery and supercapacitor currents as well as the DC bus voltage, are regulated by using the two DC/DC converters. A Lyapunov-function-based controller is proposed to regulate the DC bus voltage to its reference value. In addition, a sliding-mode controller is designed to control the battery and supercapacitor currents to their reference values. The battery current reference is generated by the energy management strategy, while the supercapacitor current reference is generated by the Lyapunov controller to ensure DC bus voltage regulation. Simulation and experimental results show that the proposed control method has satisfactory performance, including robust tracking and a smooth transition when the load power varies in the large range.

This paper describes a hybrid tram powered by a Proton Exchange Membrane (PEM) fuel cell (FC) stack supported by an energy storage system (ESS) composed of a Li-ion battery (LB) pack and an ultra-capacitor (UC) pack. This configuration allows the tram to operate without grid connection. The hybrid tram with its full load is tested in the CRRC Qingdao Sifang Co.; Ltd. It firstly works on the operation mode switching method (OPMS) without energy regenerative and proper power management. Therefore, an equivalent consumption minimization strategy (ECMS) aimed at minimizing the hydrogen consumption is proposed to improve the characteristics of the tram. The results show that the proposed control system enhances drivability and economy, and is effective for application to this hybrid system.

Massive data storage is an advanced function in a fully functional battery management system (BMS). Reducing the recording signal length undoubtedly saves the precious memory space for BMS. And it also reduces the network and computation loads. However, it leads to a side effect that the trend of signal distortion is enhanced. The optimal recording frequency in practice should be as low as possible on the condition that little signal distortion happens. This paper presents a novel method which uses a multi-frequency recording technology that cooperates two approaches according to the signal dynamics. A flexible recording frequency method is applied for stationary signals which only records signals when their values are changed. While for dynamic signals, the most dynamic period is found using discrete wavelet transformation (DWT) and further analyzed by fast Fourier transformation (FFT). By comparing two recording signal indicators for four different recording frequencies, we conclude that recording at 1 Hz is not qualified for the cell voltage and current during the dynamic period in our system due to the high dynamic performance of the vehicle. In the demonstrated vehicle, only by increasing the recording frequency to at least 2 Hz, can the accuracy of the recorded cell voltage achieve the level the same as the measurement accuracy in engineering. And we also verify that when the recording frequency is reduced to the optimal frequency compared to the high frequency recorded original signals, the accuracy of the SOC estimation is not influenced.

The powertrain system of a typical proton electrolyte membrane hybrid fuel cell vehicle contains a lithium battery package and a fuel cell stack. A multi-objective optimization for this powertrain system of a passenger car, taking account of fuel economy and system durability, is discussed in this paper. Based on an analysis of the optimum results obtained by dynamic programming, a soft-run strategy was proposed for real-time and multi-objective control algorithm design. The soft-run strategy was optimized by taking lithium battery size into consideration, and implemented using two real-time algorithms. When compared with the optimized dynamic programming results, the power demand-based control method proved more suitable for powertrain systems equipped with larger capacity batteries, while the state of charge based control method proved superior in other cases. On this basis, the life cycle cost was optimized by considering both lithium battery size and equivalent hydrogen consumption. The battery capacity selection proved more flexible, when powertrain systems are equipped with larger capacity batteries. Finally, the algorithm has been validated in a fuel cell city bus. It gets a good balance of fuel economy and system durability in a three months demonstration operation.

A control-oriented model about PEM fuel cell system with anodic and cathodic exhaust gas recirculation is introduced in this paper. It contains anode and cathode system model, fuel cell stack model which contains water transport model, oxygen transport model, nitrogen transport model, voltage model and stack cooling system model. Only the water transport model is introduced here. Then the model is verified by experiment. Results show a good agreement between the simulated and measured cell voltages, cathode inlet molar water concentrations.

With advantages like high efficiency, low pollution, and convenience of structure layout, the electric vehicles have been developing to become the main research direction of the automobile industry. Compared with the traditional centralized drive mode, the distributed electric drive can improve the power transmission efficiency and the fault tolerance. As the distributed electric drive technology developing in the field of multi-axle vehicles, research for mature dynamic coordinated control strategy has been an important task of the researchers in the automobile industry. This paper was aimed at the dynamic coordinated control strategy of multi-axle vehicles. Due to the independent driving characteristics of distributed drive multi-axle vehicles, a lateral dynamic control method using electronic differential moment power steering was proposed and verified, which can improve driving reliability, steering performance and vehicle mobility. A control model of six-axle-distributed-electric-drive vehicle was set up. With the MATLAB/Simulink software, the C code of the control model was generated and eventually burnt into the vehicle control unit (VCU). Then the hardware-in-the-loop experiment was successfully implemented on the motor bench, verifying the feasibility of the control algorithm.

Accurate estimation of the capacity of a battery pack is essential for the battery management system (BMS) in electric vehicles. The SOCs and capacities of individual cells are the prerequisites for accurately estimating the capacity of a battery pack. This paper proposes quantitative analysis on how the estimation errors of individual cells’ SOCs and capacities influence the estimation error of the battery pack capacity using an approach named Capacity–Quantity diagram (C–Q diagram). The analysis concludes that the estimation error of cell SOC has more influence on the estimation error of pack capacity than the estimation error of cell capacity does. The theoretical analysis is further validated by an experiment using six NCM batteries connected in series with different initial SOC variations. The results help to guide the determination of specifications, e.g., the estimation error of the SOC and that of the capacity, during the design process of a BMS.

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

This paper presents a thermal analysis of a semi-active battery/supercapacitor (SC) hybrid energy storage system (HESS), which is used in electric vehicles (EVs), at subzero temperatures. In subzero temperature environments, EVs suffer a dramatic driving range loss due to the energy and power capability reduction of LiFePO4 batteries, as well as severe battery degradation due to Li plating. This will increase the system operation cost because the battery pack needs to be changed frequently. Based on a novel degradation model of LiFePO4 batteries, which is validated over a wide temperature range, a near-optimal energy management strategy of the HESS for on-line use is proposed using the dynamic programming (DP) approach, which minimizes the operation cost (the electricity and the battery fade costs) over a typical China Bus Drive Cycle (CBDC). The convective heating method is integrated into the DP process. Finally, the required heating of the HESS at subzero temperatures over multi-CBDC is analyzed by evaluating the system operation cost. Simulation results show that the heating process becomes increasingly necessary with increased driving range, battery price, and heating efficiency, as well as decreasing environment temperature.

A typical topology of a proton electrolyte membrane (PEM) fuel cell electric vehicle contains at least two power sources, a fuel cell system (FCS) and a lithium battery package. The FCS provides stationary power, and the battery delivers dynamic power. In this paper, we report on the multi-objective optimization problem of powertrain parameters for a pre-defined driving cycle regarding fuel economy and system durability. We introduce the dynamic model for the FCEV. We take into consideration equations not only for fuel economy but also for system durability. In addition, we define a multi-objective optimization problem, and find a quasi-optimal solution using a two-loop framework. In the inside loop, for each group of powertrain parameters, a global optimal energy management strategy based on dynamic programming (DP) is exploited. We optimize coefficients for the DP algorithm to reduce calculating time as well as to maintain accuracy. For the outside loop, we compare the results of all the groups with each other, and choose the Pareto optimal solution based on a compromise of fuel economy and system durability. Simulation results show that for a “China city bus typical cycle,” a battery capacity of 150 Ah and an FCS maximal net output power of 40 kW are optimal for the fuel economy and system durability of a fuel cell city bus.

Energy conservation is a very important task for the automotive industry. The use of hybrid electric vehicles can improve energy efficiency, thus reducing fuel consumption and carbon emissions. In this research, the performance characteristics of a novel coaxial power-split hybrid powertrain for a transit bus are presented. The power sources are a combination of a compressed natural gas (CNG) engine and supercapacitors. A mathematical model for the coaxial power-split hybrid powertrain is established. Subsequently, an analysis program is developed based on Matlab and Advisor. The parameters are specified using experimental data. Afterwards, a rule-based control strategy is designed and optimized from the viewpoint of energy efficiency. Later, the system performance is evaluated using the Chinese Transit Bus City Driving Cycle and compared to a conventional powertrain. The results indicate that the proposed coaxial power-split hybrid powertrain can fulfill the requirements of the transit bus and enhance the energy efficiency dramatically. Moreover, the average energy efficiency of the supercapacitors was found to be above 97% over the entire driving cycle. Using supercapacitors as energy storage devices for the coaxial power-split hybrid powertrain can effectively recover the kinetic energy during regenerative braking and is a good solution for transit buses that require frequent acceleration and deceleration.

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

Sub-zero startup ability remains a key barrier for commercial application of polymer electrolyte fuel cells (PEMFC), especially for automotive applications. In order to improve the startup ability and durability of fuel cells, understanding of the characteristics and mechanisms of cold start is essential, and here modeling of fuel cell cold start plays an important role. In this study, a one-dimensional model is developed to simulate the fuel cell cold start. The model includes mass transport and phase change, heat transfer and electrochemical reaction. Key features such as membrane water and local current distributions are analyzed. Based on the one-dimensional model and simulation results, a spatial reduced simplified model is developed that distinguished only n states across the cell. The simplified model inherits the key features of the one-dimensional model, while the computational cost is significantly reduced to 10% (from 216 s to 20.88 s). The one-dimensional model and simplified model are both validated by the cold start experiment and the voltage error and temperature error are within 15% and 1.2 K respectively. Thus, the proposed simplified model could be used in dynamic simulation and in further multi-scale modeling study to build a stack model.

Water management is critical for the operation of a polymer electrolyte membrane fuel cell (PEMFC). For the purposes of high power and long working-lifetime of PEMFCs, external humidifiers are always utilized as a necessary part of balance of plants to keep the imported air and fuel wet. However, they have several disadvantages, and it is beneficial to remove them so as to reduce system volume and to enhance the cold-starting capability. In this paper, a self-humidified PEMFC of an active area 250 cm2 and cell number 320 is proposed and investigated. The imported dry air on the cathode side is mixed with moisty exhaust gas by using a recirculation valve, and the dry hydrogen on the anode side is humidified by back-diffusion water through the membrane. A nonlinear model is set up based on mass transport and energy conservation equations to capture dynamics of gases in the supply and exhaust manifolds, the gas diffusion layers (GDLs), and the membrane. An analysis is conducted to investigate the influences of parameters on dynamic and stable performances. Simulation results show that system performances can be greatly affected by parameters such as air stoichiometry, current density, exhaust gas recirculation (EGR) ratio, and membrane thickness. By accurately controlling the EGR ratio and carefully selecting design and operation parameters, it is probably for a PEMFC without an external humidifier to have similar system efficiency compared to a traditional system.

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

In order to estimate the remaining driving range (RDR) in electric vehicles, the remaining discharge energy (ERDE) of the applied battery system needs to be precisely predicted. Strongly affected by the load profiles, the available ERDE varies largely in real-world applications and requires specific determination. However, the commonly-used direct calculation (DC) method might result in certain energy prediction errors by relating the ERDE directly to the current state of charge (SOC). To enhance the ERDE accuracy, this paper presents a battery energy prediction (EP) method based on the predictive control theory, in which a coupled prediction of future battery state variation, battery model parameter change, and voltage response, is implemented on the ERDE prediction horizon, and the ERDE is subsequently accumulated and real-timely optimized. Three EP approaches with different model parameter updating routes are introduced, and the predictive-adaptive energy prediction (PAEP) method combining the real-time parameter identification and the future parameter prediction offers the best potential. Based on a large-format lithium-ion battery, the performance of different ERDE calculation methods is compared under various dynamic profiles. Results imply that the EP methods provide much better accuracy than the traditional DC method, and the PAEP could reduce the ERDE error by more than 90% and guarantee the relative energy prediction error under 2%, proving as a proper choice in online ERDE prediction. The correlation of SOC estimation and ERDE calculation is then discussed to illustrate the importance of an accurate ERDE method in real-world applications.

This paper presents a robust gain-scheduled H∞ controller to improve lateral stability and handling of four-wheel-independent-drive electric vehicles possessing active front steering system with considerations of state delay. The time delay for the state is assumed as uncertain time-invariant but has a known constant bound. By considering the tyre cornering stiffness represented by the norm-bounded uncertainty and the time-varying longitudinal velocity described by a polytope with finite vertices, and the vehicle lateral dynamics model is converted into the uncertain vehicle linear parameter-varying LPV state-delayed system. The resulting delay-dependent robust gain-scheduling state feedback H∞ controller is finally designed utilizing the constructed Lyapunov-Krakovskii functional, and solved via a set of delay-dependent linear matrix inequalities LMIs. Simulations for various driving scenarios are implemented using MATLAB/SIMULINK-CARSIM. The simulation results show the effectiveness of the proposed controller.

Drive wheel systems combined with the in-wheel permanent magnet synchronous motor (I-PMSM) and the tire are highly electromechanical-coupled. However, the deformation dynamics of this system, which may influence the system performance, is neglected in most existing literatures. For this reason, a deformable tire and a detailed I-PMSM are modeled using Matlab/Simulink. Furthermore, the influence of tire/road contact interface is accurately described by the non-linear relaxation length-based model and magic formula pragmatic model. The drive wheel model used in this paper is closer to that of a real tire in contrast to the rigid tire model which is widely used. Based on the near-precise model mentioned above, the sensitivity of the dynamic tire and I-PMSM parameters to the relative error of slip ratio estimation is analyzed. Additionally, the torsional and longitudinal vibrations of the drive wheel are presented both in time and frequency domains when a quarter vehicle is started under conditions of a specific torque curve, which includes an abrupt torque change from 30 N · m to 200 N · m. The parameters sensitivity on drive wheel vibrations is also studied, and the parameters include the mass distribution ratio of tire, the tire torsional stiffness, the tire damping coefficient, and the hysteresis band of the PMSM current control algorithm. Finally, different target torque curves are compared in the simulation, which shows that the estimation error of the slip ratio gets violent, and the longitudinal force includes more fluctuation components with the increasing change rate of the torque. This paper analyzes the influence of the drive wheel deformation on the vehicle dynamic control, and provides useful information regarding the electric vehicle traction control.

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

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

This paper utilizes the dynamic programming (DP) approach to deal with the integrated optimization problem for deriving the best configuration and energy split strategies of a hybrid energy storage system (HESS), including a battery and a supercapacitor (SC), for an electric city bus. Within the optimization process, a preset cost function is employed to evaluate the HESS life cycle cost based on a dynamic degradation model of the LiFePO4 battery, which is initially proposed by us. For system hybridization, the battery size is optimized according to the requested minimal mileage, while the optimal configuration of the SC pack (i.e., the numbers of the SC modules in series and parallel) is determined using the DP approach. It is shown that the life cycle cost of the HESS initially decreases rapidly with the addition of SCs, though the rate of this reduction decreases as the amount of SC increases. The HESS candidates occurring in the transition area can therefore be regarded as the best solutions. For the energy split strategy, several control rules can be extracted from the DP results, and a near-optimal rule-based strategy is proposed in this paper. When compared to the battery-only configuration, the HESS controlled by the rule-based strategy can reduce 47% and 60% of the ESS life cycle cost along the typical China Bus Driving Cycle and the Urban Dynamometer Driving Schedule, respectively. This paper also proves that a well-tuned rule-based strategy, which can be easily implemented in a vehicle, presents rather good performance when compared to the DP approach. In addition, the proposed strategy performance can be further improved by increasing the SC usage.

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

This paper deals with the real-time energy management strategies for a hybrid energy storage system (HESS), including a battery and a supercapacitor (SC), for an electric city bus. The most attractive advantage deriving from HESSs is the possibility of reducing the battery current stress to extend its lifetime. To quantitatively compare the effects of different control strategies on reducing battery degradation, a dynamic degradation model for the LiFePO4 battery is proposed and validated in this paper. The battery size is optimized according to the requested minimal mileage, while the size of SC is optimized based on the power demand profile of the typical China Bus Driving Cycle (CBDC). Based on the optimized HESS, a novel fuzzy logic controller (FLC) and a novel model predictive controller (MPC) are proposed and compared with the existing rule-based controller (RBC) and filtration based controller (FBC), after all the controllers are tuned to their best performance along the CBDC. It turns out that FLC and RBC achieve the best performance among the four controllers, which is validated by the DP-based result. Furthermore, about 50% of the HESS life cycle cost is reduced in comparison with the battery-only configuration. In addition, the controllers are also compared along the New European Driving Cycle (NEDC), which represents another normalized driving cycle. The results show that the RBC, MPC, and FLC achieve a similar performance, and they reduce about 23% of the HESS life cycle cost when compared to the battery-only configuration. The RBC and FLC are regarded as the best choices in practical applications due to their remarkable performance and easy implementation.

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

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

The independent driving wheel system, which is composed of in-wheel permanent magnet synchronous motor(I-PMSM) and tire, is more convenient to estimate the slip ratio because the rotary speed of the rotor can be accurately measured. However, the ring speed of the tire ring doesn’t equal to the rotor speed considering the tire deformation. For this reason, a deformable tire and a detailed I-PMSM are modeled by using Matlab/Simulink. Moreover, the tire/road contact interface(a slippery road) is accurately described by the non-linear relaxation length-based model and the Magic Formula pragmatic model. Based on the relatively accurate model, the error of slip ratio estimated by the rotor rotary speed is analyzed in both time and frequency domains when a quarter car is started by the I-PMSM with a definite target torque input curve. In addition, the natural frequencies(NFs) of the driving wheel system with variable parameters are illustrated to present the relationship between the slip ratio estimation error and the NF. According to this relationship, a low-pass filter, whose cut-off frequency corresponds to the NF, is proposed to eliminate the error in the estimated slip ratio. The analysis, concerning the effect of the driving wheel parameters and road conditions on slip ratio estimation, shows that the peak estimation error can be reduced up to 75% when the LPF is adopted. The robustness and effectiveness of the LPF are therefore validated. This paper builds up the deformable tire model and the detailed I-PMSM models, and analyzes the effect of the driving wheel parameters and road conditions on slip ratio estimation.

Distributed electric vehicles are typical over- actuated systems, therefore, the optimal torque distribution can be realized through the driving wheels. It is meaningful to extend driving range and reinforce stability of vehicles by using optimal torque allocation methods. In this paper, a quadratic programing based torque allocation strategy is proposed to minimize the drivetrain power loss and utilize the tire-road friction of each wheel reasonably. A co-simulation using CarSim and Simulink is conducted to prove the effectiveness of the proposed algorithm under different conditions.

The lithium titanium oxide (LTO) anode is widely accepted as one of the best anodes for the future lithium ion batteries in electric vehicles (EVs), especially since its cycle life is very long. In this paper, three different commercial LTO cells from different manufacturers were studied in accelerated cycle life tests and their capacity fades were compared. The result indicates that under 55 °C, the LTO battery still shows a high capacity fade rate. The battery aging processes of all the commercial LTO cells clearly include two stages. Using the incremental capacity (IC) analysis, it could be judged that in the first stage, the battery capacity decreases mainly due to the loss of anode material and the degradation rate is lower. In the second stage, the battery capacity decreases much faster, mainly due to the degradation of the cathode material. The result is important for the state of health (SOH) estimation and remaining useful life (RUL) prediction of battery management system (BMS) for LTO batteries in EVs.

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

In this paper, the vehicle lateral motion control of four-wheel-independent-drive electric vehicles (4WID-EVs) with combined active front steering (AFS) and direct yaw moment control (DYC) through in-vehicle networks is studied. As a typical over-actuated system, a 4WID-EV requires a control allocation algorithm to achieve the generalized control efforts. In this paper, a quadratic programming (QP) based torque allocation algorithm is proposed with the advantage of equally and reasonably utilizing the tire-road friction of each wheel. It is also well known that the in-vehicle network and x-by-wire technologies have considerable advantages over the traditional point-to-point communications, and bring great strengths to complex control systems such as 4WID-EVs. However, there are also bandwidth limitations which would lead to message time-delays in in-vehicle network communications and degradation of control performance. The paper also proposes a mechanism to effectively utilize the limited network bandwidth resources and attenuate the adverse impact of in-vehicle network-induced time-delays, based on the idea of dynamic message priority scheduling. Simulation results from a high-fidelity vehicle model show that the proposed control architecture with the torque allocation algorithm and message dynamic-priority scheduling procedure can effectively improve the vehicle lateral motion control performance, and significantly reduce the adverse impact of the in-vehicle network message time-delays in the simulated maneuvers.

Recovering the kinetic energy of a vehicle is one inherent advantage of an electric vehicle. A permanent-magnet synchronous motor is widely adopted for the traction motor in an electric vehicle with the advantage of a high efficiency and a high torque density. The principle for electric braking control of the permanent-magnet synchronous motor under field-oriented control is studied. The efficiency model of the electric drive system, which is different from that of the internal-combustion engine drive system, can be exactly described by analytical equations. On this basis, the battery power can be expressed as a function of the angular velocity and the electromagnetic torque of the motor. By solving the partial differential equation for the battery power, the instantaneous optimal regenerative braking torque of the permanent-magnet synchronous motor is simply calculated according to the vehicle braking torque demand and the motor speed. Compared with the existing efficiency map method, the analytical technology is easily implemented. Then a four-wheel-drive electric vehicle is investigated to achieve optimal regenerative braking control. The dynamic behaviour of braking in the four-wheel-drive electric vehicle is also considered. The parallel braking pattern and the series braking pattern are investigated in order to evaluate the availability of braking energy recovery. The instantaneous optimal regeneration energy can be recovered for the series braking system, and a significant amount of energy can be recovered for the parallel braking system by adjusting the free travel of the brake pedal.

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

Uncoordinated charging load of electric vehicles (EVs) increases the peak load of the power grid, thereby increasing the cost of electricity generation. The valley-filling charging scenario offers a cheaper alternative. This study proposes a novel decentralized valley-filling charging strategy, in which a day-ahead pricing scheme is designed by solving a minimum-cost optimization problem. The pricing scheme can be broadcasted to EV owners, and the individual charging behaviors can be indirectly coordinated. EV owners respond to the pricing scheme by autonomously optimizing their individual charge patterns. This device-level response induces a valley-filling effect in the grid at the system level. The proposed strategy offers three advantages: coordination (by the valley-filling effect), practicality (no requirement for a bidirectional communication/control network between the grid and EV owners), and autonomy (user control of EV charge patterns). The proposed strategy is validated in simulations of typical scenarios in Beijing, China. According to the results, the strategy (1) effectively achieves the valley-filling charging effect at 28% less generation cost than the uncoordinated charging strategy, (2) is robust to several potential affecters of the valley-filling effect, such as (system-level) inaccurate parameter estimation and (device-level) response capability and willingness (which cause less than 2% deviation in the minimal generation cost), and (3) is compatible with device-level multi-objective charging optimization algorithms.