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

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

Commercial large format high-nickel/silicon-graphite (NCM811/SiC) lithium-ion batteries have been applied in long range electric vehicles for their exceptional high energy density. However, fire and explosions caused by these high-energy batteries arouse safety concerns. Mathematical model is a powerful method to study and predict the hazardous thermal behaviors but have not been well established due to lack of the detailed side reaction sequence and kinetics of the NCM811/SiC chemistry. This paper reveals that the thermal interactions between the high energy materials dominate the heat generation process and determines the detailed side reaction sequence and thermal kinetics based on experiments. A cell thermal runaway model considering the reaction sequence is then established based on the kinetics and achieves accurate prediction of the cell thermal behaviors. The validated model is further employed to investigate the thermal deterioration originated from high-energy NCM811/SiC chemistry. According to the simulations, the thermal interactions between SiC-electrolyte, NCM811-electrolyte and NCM811-SiC can lead to maximum temperature increase by 318 °C, 222 °C and 174 °C, respectively, with total heat rising by 29%, 20% and 17%, when compared with the conventional Li[Ni1/3Co1/3Mn1/3]O2/graphite chemistry.

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

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

Thermal runaway (TR) is a major safety concern in lithium-ion batteries. Model-based TR prediction is critically needed to optimize safety designs of cells. This paper presents a novel scheme for developing reliable battery TR model from kinetics analysis of cell components. First, differential scanning calorimetry (DSC) tests on the individual cell components and their mixtures are conducted to reveal the TR mechanism and characterize the exothermic reactions, of which the major six (such as the decomposition of solid electrolyte interface (SEI) film) are determined as the dominant heat sources. The kinetics parameters of each exothermic reactions are identified from the DSC tests results at variant heating rates using Kissinger’s method and nonlinear fitting method. A predictive battery TR model is established by superimposing the chemical kinetics equations of the six exothermic reactions. The model fits well with the adiabatic TR test results and the oven tests results of a 24 Ah lithium-ion battery, indicating that the model can well reflect the battery TR mechanism and be trusted to predict battery safety performance without assembling a real battery.

Cyclic voltammetry and linear sweep voltammetry are the most commonly used diagnosis methods to estimate the internal statement of fuel cell stack. However, both methods can only be applied in a single fuel cell. There is a lack of suitable in situ diagnosis methods for a multi-burl fuel cell stack. The galvanostatic charge method (GSC) is a very convenient in situ diagnosis method, which can be applied to a multi-burl fuel cell stack to calculate the electrochemical active surface area (ECSA), double-layer capacitance, and hydrogen crossover current. However, there are not enough experiments to analyze the adaption of GSC or apply this method to analyze fuel cell degradation process. In this study, we conducted experiments to validate the accuracy of GSC under different test conditions, and proposed a new correction algorithm to improve the accuracy. Next, this method was applied to analyze the performance degradation process of a four-cell stack. The experimental results showed that the estimated GSC parameters exactly coincide with the standard values. Additionally, for the degradation analysis of a four-cell stack, the GSC results showed that the ECSA reduction of cell four is the direct reason for performance degradation. Moreover, the assumptions of ECSA reduction and carbon corrosion were validated by material experiments. About 5° decrease in the contact angle of the gas diffusion layer (GDL) in the cathode catalyst was observed in the worst cell.

Accurate measurement of the characteristic temperatures of thermal runaway, which are affected by many factors, is important for battery safety evaluation. A one-dimensional thermal resistance network model is built in this study to investigate the influences of various factors on the thermal runaway features of lithium-ion batteries. In the model, the battery is divided into four independent components in the thickness direction, with thermal resistances connecting different nodes. The gas thermal resistance is added to simulate swelling and rupture of the battery. The model can effectively fit the battery thermal runaway behavior under both adiabatic thermal runaway and oven test conditions. Model-based analyses show that the thermal runaway features and characteristic temperatures are significantly affected by the test conditions, thermocouple positions, and battery thickness. The onset temperature of thermal runaway (T2) obtained in the oven test is 48.1 °C lower than that obtained in the adiabatic thermal runaway test. The measured T2 varies at different positions, and the difference can exceed 20% when the battery thickness increases to 10 cm. Moreover, the maximum thermal runaway temperature (T3) at the surface is approximately half that at the other positions. Finally, several suggestions for reasonable thermocouple placement are proposed, which can provide useful guidance for accurately evaluating battery thermal runaway performance.

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.

Fast charging is critical for the application of lithium-ion batteries in electric vehicles. Conventional fast charging algorithms may shorten the cycle life of lithium-ion batteries and induce safety problems, such as internal short circuit caused by lithium deposition at the negative electrode. In this paper, a novel, non-destructive model-based fast charging algorithm is proposed. The fast charging algorithm is composed of two closed loops. The first loop includes an anode over-potential observer that can observe the status of lithium deposition online, whereas the second loop includes a feedback structure that can modify the current based on the observed status of lithium deposition. The charging algorithm enhances the charging current to maintain the observed anode over-potential near the preset threshold potential. Therefore, the fast charging algorithm can decrease the charging time while protecting the health of the battery. The fast charging algorithm is validated on a commercial large-format nickel cobalt manganese/graphite cell. The results showed that 96.8% of the battery capacity can be charged within 52 min. The post-mortem observation of the surface of the negative electrode and degradation tests revealed that the fast charging algorithm proposed here protected the battery from lithium deposition.

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.

Power split configurations exhibit potential on decreasing fuel consumption. However, how to select the best power split configuration among all the possible configurations still need to be explored. In this paper, a one-dimensional searching ECMS (Equivalent Consumption Minimization Strategy) is developed to evaluate fuel consumption for input power split configuration. Additionally, a battery degradation model based on experimental data is applied to evaluate the battery degradation for input power split configuration. According to one-dimensional searching ECMS and battery degradation model, the impact of different input power split configurations on fuel consumption and battery degradation is investigated. Simulations show that configuration T1 has fuel consumption and battery degradation advantages for PHEV city bus application in China. These advantages are observed because the system efficiency operation points of configuration T1 are closer to the high efficiency area than the system efficiency operation points of other input power split configurations. Furthermore, according to the lever model and one-dimensional searching ECMS method, a power split configuration selection method is provided in this paper. The simulation results based on configuration selection method still indicate that configuration T1 appears to have the best fuel economy for PHEV city bus application. Moreover, configuration T5, with a lever length close to 0, exhibits the worst fuel consumption and battery degradation.

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.

There are many types of high-power batteries used in HEVs, and their durabilities and degradation mechanisms are different. In this paper, four types of commercial high-power batteries, including two types of LTO/NCM lithium-ion battery from two different manufacturers, a C/LMO battery and a supercapacitor (SC), are studied. A durability test with a realistic current profile for an HEV is used so that the durability results more closely reflect real operating conditions than a general cycle life test. Incremental capacity (IC) curves are used to qualitatively analyze the degradation mechanism. To compensate for defects in the IC method, a prognosis model, using a genetic algorithm to reconstruct constant current charge voltage curves, is adopted to quantitatively identify the battery aging mechanism.

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 capacity degradation of the lithium ion battery should be well predicted during battery system design. Therefore, high-fidelity capacity degradation models that are suitable for the task of capacity prediction are required. This paper proposes a novel capacity degradation model that can simulate the degradation dynamics under varying working conditions for large-format lithium ion batteries. The degradation model is built based on a mechanistic and prognostic model (MPM) whose parameters are closely linked with the degradation mechanisms of lithium ion batteries. Chemical kinetics was set to drive the parameters of the MPM to change as capacity degradation continues. With the dynamic parameters of the MPM, the capacity predicted by the degradation model decreases as the cycle continues. Accelerated aging tests were conducted on three types of commercial lithium ion batteries to calibrate the capacity degradation model. The good fit with the experimental data indicates that the model can capture the degradation mechanisms well for different types of commercial lithium ion batteries. Furthermore, the calibrated model can be used to (1) evaluate the longevity of a battery system under a specific working load and (2) predict the evolution of cell variations within a battery pack when different cell works at different conditions. Correlated applications are discussed using the calibrated degradation model.

Early detection of an internal short circuit (ISC) in lithium ion batteries has become a crucial task for battery management, as ISC is believed to be the root cause of several large format lithium ion battery fire accidents. In this paper, a scheme of on-line detection of ISC is proposed, and the online ISC detection problem is addressed from a model parameterization and parameter estimation perspective. Using a 3D electrochemical-thermal-ISC coupled model, we explore the correlation between the measured voltage, current, and temperature data and the ISC status. It is identified that the abnormal depletion in the state-of-charge (SOC) and excessive heat generation associated with ISC affect the voltage and temperature responses, and that the correlation can be captured by a properly parameterized phenomenological model. The ISC detection is then recast as a parameter estimation problem, for which a model-based estimation algorithm is proposed and evaluated. It is shown that the estimation algorithm can track the parameter variations in real-time, thereby making it feasible to track ISC incubation status or to detect instantaneously triggered ISC. Moreover, it is observed that the recorded temperature profile is not affected by the location where the ISC occurs, due to the oval shape of the temperature distribution caused by anisotropic heat conduction of the battery core. Therefore, the proposed algorithm can detect the ISC, regardless of its physical location within the battery.

Low temperature charging is a major challenge for lithium-ion batteries, since it could lead to dramatic performance degradation and potential safety issues. A pre-heating process is usually applied to overcome above-mentioned challenges. Pulsed operation is adopted as one of established internal pre-heating methods with good temperature uniformity. Herein we employed and investigated bidirectional pulsed current through experimental methods to obtain the main data of the thermal action for comprehensively analyzing heat generation characteristics and thermoelectric coupling model based on second-order RC circuit to verify the basic principle. Battery durability research was then conducted via a continuous heating test method which enables rapid testing of capacity degradation. The results indicated that proposed pulsed heating could not significantly damage the life span from the perspective of long-term applications: the battery has only 1% capacity decay after 170 h continuous heating with a heating rate of 11 °C/min. Parameters, which are beneficial for heating rate, are further found to be detrimental for degradation and vice versa. Nevertheless, based on the outcome of this study, the pulse waveform with a shorter period and a higher amplitude are suggested to give an optimal combination of higher heating rate and lower degradation. Such a pulsed heating method has several potential application scenarios, ranging from electric vehicles and even stationary storage systems.

Plug-in electric vehicles (PEV) appears to have sales momentum in major personal vehicle markets but are still at the early market stage. Opportunities to accelerate PEV adoption can be discovered through comprehensive total cost of ownership (TCO) analysis. Understanding the cost-effective electric ranges of PEVs for consumers, manufacturers, and the society is critical for any discussion of PEV mass markets. This study expanded the traditional TCO approach by (1) fully considering heterogeneous consumer driving patterns, (2) quantifying the charging inconvenience and range anxiety cost of battery-electric vehicles (BEVs), and (3) monetizing both tangible and intangible PEV policies. Uncertainties were handled through Monte Carlo simulation. The results suggest that BEVs with an electric range of 250–350 km have the lowest TCO in cities with government-enacted purchase limitations, and internal combustion engine vehicles (ICEVs) have the lowest TCO in cities without purchase limitations, even when considering PEV subsidies. The lowest TCO for some consumer groups is obtained by BEVs with an electric range of 400–450 km, especially in northern China, where the weather is colder. The cost-effective all-electric range for BEVs in each city in 2025 will decrease due to improved battery performance in cold environments and an expanded charging infrastructure. Based on TCO, plug-in hybrid electric vehicles (PHEVs) are currently more suitable for drivers with a high average daily mileage or a large mileage variance. However, by 2025, BEVs with a long driving range may become a more cost-effective choice for these drivers.

DC microgrid is supposed to be a feasible solution to reduce the negative impact of electric vehicle (EV) fast charging on the electric grid and improve the penetration of photovoltaics (PV) generation. In this paper, an improved decentralized Virtual-battery based droop control with the capability of bus voltage maintenance, load power dispatch and SOC balance of the energy storage system (ESS) is proposed to ensure the autonomous and stable operation of the DC microgrid. The reference output voltage and virtual resistance in the droop control loop are altered dynamically based on the Virtual-battery model of the ESS. The coordinated control among the PV-ESS-Grid integrated system is realized through the primary Bus-Signaling control, where the reference voltages at which the control modes of the PV array and the grid are switched are designed based on the VirtualOCV of the ESS. The effectiveness of the proposed control strategy is validated in MATLAB/Simulink environment with an equivalent bus capacitance-based model where the EV charging profile is obtained from real-world charging data of a fast charging station. The merits of the control strategy including higher PV utilization, less frequent connection of the grid and more precise voltage tracking are highlighted in comparison with the conventional droop control strategy. Finally, the sizing of the ESSs is optimized based on the total cost of the DC microgrid, including the daily electricity cost purchased from the grid and the depreciation cost of the ESSs based on the expanded capacity degradation model of Li-ion batteries.

Internal short circuit is among the most common causes of thermal runaway in lithium-ion batteries. Substitute triggering approaches are of great significance to internal short circuit research. This paper compares the performance of five substitute triggering approaches for internal short circuit, including (1) triggering with phase-change materials, (2) shape-memory alloys, (3) using artificially induced dendrite growth, (4) equivalent resistance, and (5) nail penetration. The thermal-electrical coupled features, controllability, similarity to real accidents and repeatability of the test are discussed by experimental and modelling analysis. The results show that the triggering approaches with phase-change materials and shape-memory alloys are controllable to trigger specific types of the internal short circuit but complex in experimental preparation. The triggering approach by artificially induced dendrite growth may best simulate the self-induced internal short circuit in real-time applications but with poor controllability. The triggering approach using equivalent resistance can be beneficial for calibrating the electrochemical-thermal coupled model, but no real internal short circuit in tests. Nail penetration is the easiest one to be conducted but has poor repeatability. The four classes of the internal short circuit are analyzed to reveal the relationship between physic phenomenon and thermal runaway. This paper guides the study of internal short circuit mechanism and safety evaluation.

This paper conducts research on the fuel accommodation control of a fuel-flexible engine. Blended fuels from gasoline and diesel are tested on a compression ignition engine. Two problems with the fuel-flexible engine are studied. One problem is that the engine brake torque output is different with different fuels, and the other problem is that the thermal efficiency deteriorates without recalibration. One open-loop control method and one closed-loop control method based on cylinder pressure sensor are proposed to deal with the fuel accommodation control problems. The open-loop control method features fuel blend ratio detection, which is a new dimension of input for the conventional MAP based engine management system. Gasoline and diesel blend ratio detection methods are studied, and the result also provides information for the closed loop control method as a feedforward controller. An indicated mean effective pressure based method is proposed for the engine brake torque difference problem, and a feedback controller with indicated mean effective pressure maximum seeking algorithm is used to optimize engine efficiency. The fuel injection amount and timing will be adjusted according to the fuel estimation result and the feedback control algorithms. Results show that the methods proposed could identify fuel blend ratio quite accurately, the engine brake torque output could follow the target value well, and the engine thermal efficiency could be optimized.

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.

This study assesses the energy reduction associated with Hybrid Electric Vehicles (HEVs), Plug-in Hybrid Electric Vehicles (PHEVs) and Battery Electric Vehicles (BEVs) compared to conventional vehicles (CVs) for real-world driving conditions in a specific geographic region (Beijing, China). To understand the driving patterns in Beijing, a passenger car travel survey has been conducted since 2012, including over 1000 vehicles. The initial results from driving range distribution have been calculated. In this study, first, a Utility Factor and the typical driving cycles based on 2000 days’ worth of Global Position System (GPS) data are analyzed. Next, the real-world energy consumption of CVs, HEVs, PHEVs and BEVs are simulated. Finally, the fuel consumption of vehicles under different driving patterns is compared to provide data on the optimal electric vehicles and reliable test cycles for Beijing. We find that electric vehicles in Beijing, including HEVs, PHEVs and BEVs, yield more fuel reduction benefits than in the U.S. because of the severe driving conditions and short driving ranges. For PHEVs in Beijing, smaller batteries, corresponding to a 30–50 km Charging Depleting (CD) range, are preferred to meet the demands of most drivers and add less extra cost to the vehicle. We also confirm that the Chinese current suggested label values based on NEDC cycle underestimate the fuel consumption of vehicles and fuel reduction benefits of electric vehicles in Beijing. This study addresses the importance of developing and using the real-world driving cycles in designing and evaluating electric vehicles.

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.

Thermal runaway (TR) propagation in a large format lithium ion battery pack can cause disastrous consequences and thus deserves study on preventing it. A lumped thermal model that can predict and help prevent TR propagation in a battery module using 25 Ah LiNi x Co y Mn z O 2 large format lithium ion batteries has been built in this paper. The TR propagation model consists of 6 fully-charged single batteries connected through thermal resistances and can fit experiment data well. The modeling analysis focuses on discussing the influences on the TR propagation process caused by changes in different critical modeling parameters. The modeling analysis suggests possible solutions to postpone and prevent TR propagation. The simulation shows that it might be better to choose proper parameters that help prevent TR propagation rather than just postpone it, because a delay in the TR propagation process leads to a higher level of heat gathering which may cause severer thermal hazards. To prevent TR propagation, the model provides some substantial quantified solutions: (1) raise the TR triggering temperature to higher than 469 °C; (2) reduce the total electric energy released during massive internal short circuit to 75% or less of its original value; (3) enhance the heat dissipation by increasing the heat dissipation coefficient to higher than 70 W m −2 K −1 ; (4) add extra thermal resistant layers between adjacent batteries with a thickness of 1 mm and a thermal conductivity less than 0.2 W m −1 K −1 . One implementation, which is verified by experiment, is to insert thermal resistant layer between adjacent batteries to prevent TR propagation in the battery module.

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 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.

Challenges remain in diesel low temperature combustion implementation due to combustion inconsistency or instability. One possible solution is the introduction of closed-loop combustion control systems. In this paper, an in-cycle control method is proposed, which tries to bring the tricky combustion under control by reducing fluctuations of combustion center. Firstly, an SOC (Start of Combustion) online detection method based on the reconstruction of the drag pressure trace and detection of the real-time in cylinder pressure differences with the ideal drag pressure trace is presented. The drag pressure trace estimation measure proposed features a step-by-step update of estimation reference and equivalent adiabatic coefficient. Due to this step-by-step method, the pressure difference threshold for SOC detection is reduced to 0.2 MPa, with both good real-time performance and reliability. Secondly, the SOC detection method is applied in an in-cycle combustion control case under double-injection circumstance. Experiments are performed to find the relationship between SOC, combustion center and exhaust recirculation rate. An algorithm which adaptively adjusts the second injection timing according to the SOC of the pilot injection is designed according to the relationship obtained through experiments. Test results indicate that the fluctuations of the combustion center is successfully reduced.

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.

This paper proposes an optimal energy management strategy based on the approximate Pontryagin’s Minimum Principle (A-PMP) algorithm for parallel plug-in hybrid electric vehicles (HEVs). When the driving cycles are known in advance, the Pontryagin’s Minimum Principle (PMP) can help to achieve the best fuel economy, but real-time control has been unavailable due to the massive computational load required by instantaneous Hamiltonian optimization. After observing some regular patterns in numeric PMP results, we were inspired to apply a novel piecewise linear approximation strategy by specifying the turning point of the engine fuel rate for the Hamiltonian optimization. As a result, the instantaneous Hamiltonian optimization becomes convex. Considering the engine state, there are only five candidate solutions for the optimization. For the engine off state, only one of the available torque split ratios (TSR) is one of these five candidates. The other four TSR candidates are for the engine on state, including the TSR when the engine operates at the best efficiency point for the current speed, the TSR when the engine delivers all the required torque and two terminal TSRs. The optimal TSR is the one with the smallest Hamiltonian of the current engine state. The engine state with the smallest Hamiltonian will be requested for the next time step. The results show that the A-PMP strategy reduced fuel consumption by 6.96% compared with the conventional “All-Electric, Charge-Sustaining” (AE–CS) strategy. In addition, the A-PMP shortened the simulation time from 6 h to only 4 min, when compared with the numeric PMP method. Unlike other approximation methods, the proposed novel piecewise linear approximation caused no severe distortion to the engine map model. The engine state switching frequency is also reduced by 43.40% via both the filter and the corresponding engine on/off optimal control strategy.

Battery electric vehicles with a range of more than 500 km are expected to become increasingly competitive in the future. The energy density of the currently available lithium batteries should be significantly increased to support the operation of such vehicles, and high-power charging is required to reduce the charging time. However, high-power charging may negatively affect the durability and safety of lithium batteries because of increased heat generation, capacity fading, and lithium plating, which can induce the risk of battery thermal runaway. Currently, there are no established boundary conditions for high-power charging or methods for evaluating its risks, especially in case of high-capacity lithium batteries. This study proposes a test procedure for examining the reaction characteristics of the capacity fading and thermal tolerance of lithium batteries that are subjected to high-power charging. Further, the migration characteristics of the temperature threshold of battery thermal runaway are investigated using the proposed procedure. The test results demonstrate that high-power charging significantly impacts the durability and thermal safety of the high-capacity lithium batteries. In particular, the capacity fading rate can reach up to 30% only after 100 charge cycles depending on the battery type. Furthermore, the thermal tolerance can decrease up to 40% by considering the change in the self-heating temperature as an indicator. Based on the study results, it can be concluded that the thermal management systems should be carefully designed to satisfy the high-power charging requirements. Otherwise, the high power charging only can be performed with limited range for battery electric cars with long all electric range.

Identification of cell SOC (state-of-charge) inconsistency for LiFePO4 battery packs is challenging due to the demanding conditions in hybrid electric vehicles (HEVs) and the relatively flat SOC–OCV (open circuit voltage) curve of LiFePO4 cells compared to others. We experimentally investigate cell voltages in a small battery pack and propose a Mean-plus-Difference Model (M+D Model). The M+D Model uses a cell mean model (CMM) representing the overall performance of the pack in high frequency. Meanwhile cell voltage differences (CVDs) between cells and the “mean cell” are studied by a cell difference model (CDM) in low frequency. A CDM considering SOC and internal resistance differences is subsequently presented and OCV differences are estimated. We further propose an SOC strategy to accurately identify cell SOC inconsistency by intermittently lowering pack SOC to 30% during HEV operation. Finally we discover that SOC differences can be determined with estimated OCV differences using SOC-difference/OCV-difference curve. The proposed method is verified by simulation and experiment. With the proposed method, LiFePO4 cell SOC inconsistency can be precisely estimated with existing measuring technology during HEV operating and cell equalization can be ultimately implemented.

HCCI (Homogeneous Charge Compression Ignition) combustion has the potential to significantly reduce NO x and PM emission of diesel engines due to its nature of even mixture and low temperature combustion. This combustion mode is only applicable in part load and low speed area so far. This range limitation results in frequently combustion mode switching from HCCI to CI and vice versa in real applications. HCCI realized by late injection with heavy EGR rate is investigated, which is also named PCCI (Premixed Charged Compression Ignition) sometimes. By changing control parameters (including injection timing, injection pressure and EGR rate) individually, the influences of these parameters on HCCI combustion and emissions are identified. Based on the evaluation of the influences, one pre-defined step-by-step air–fuel coordination strategy is presented. Additionally one hysteresis combustion mode determination method is proposed to avoid target mode fluctuation due to variation of engine speed or engine load in a small range. Torque deficiency occurs in acceleration transients as a result of fuel limitation to reduce soot emission, especially during HCCI to CI transitions. One ISG (Integrated Starter Generator) motor is applied to perform dynamic torque compensations in transient operations. Engine friction torque is estimated by means of cylinder pressure information from in-cylinder pressure sensors. On the basis of torque estimation, the difference between desired engine brake torque and actual engine brake torque is obtained. The torque output of ISG motor accounts for this torque gap and the torque pre distributed to ISG motor. The strategy is verified by engine tests. Experimental results indicate that, with the finely defined air–fuel coordination, less unstable combustion occurs and the target modes are established more quickly during transitions both in steady states and in transients. The torque deficiency during acceleration operations could be well compensated by the ISG motor.

This paper presents an optimal sizing method for plug-in proton exchange membrane (PEM) fuel cell and lithium-ion battery (LIB) powered city buses. We propose a theoretical model describing the relationship between components’ parameters and vehicle performance. Analysis results show that within the working range of the electric motor, the maximal velocity and driving distance are influenced linearly by the parameters of the components, e.g. fuel cell efficiency, fuel cell output power, stored hydrogen mass, vehicle auxiliary power, battery capacity, and battery average resistance. Moreover, accelerating time is also linearly dependant on the abovementioned parameters, except of those of the battery. Next, we attempt to minimize fixed and operating costs by introducing an optimal sizing problem that uses as constraints the requirements on vehicle performance. By solving this problem, we attain several optimal sizing rules. Finally, we use these rules to design a plug-in PEM fuel cell city bus and present performance results obtained by on-road testing.

HCCI combustion can substantially reduce NOx and soot emissions of diesel engines. However it is hard for diesel HCCI to obtain the same level of fuel economy as traditional diesel engines. This paper presents a way to improve the fuel economy of diesel HCCI by adopting Integrated Starter Generator (ISG). A set of diesel parallel hybrid power system (in this article, we name it “hybrid engine”) is constructed by installing an ISG motor on a diesel engine, which applies HCCI–CI combined combustion. Based on a lot of investigation on diesel HCCI, this paper is focus on improving HCCI engine performance, especially the fuel economy, by adopting ISG motor assist control. The coordination control strategies of engine and ISG motor in HCCI transient states, HCCI–CI transition, engine quick start and braking energy regeneration are developed. The results of equivalent New European Driving Cycle (NEDC) tests show that, by using ISG assistance, the fuel consumption of the diesel HCCI engine is greatly reduced. Meanwhile the NOx and soot emission are also improved.

The effect of synthetic diesel fuel made from natural gas (Gas to Liquid, GTL) on the engine performances (such as power, efficiency and emission) was carried out on one Euro III common rail (CR) heavy duty (HD) diesel engine without any modification. The results showed that the engine fueled with GTL had some variations compared with the one fueled with petroleum-based low sulfur diesel fuel (sulfur content less 50 ppm). The maximum torque and power were decreased by 1.3% and 1.9%, respectively. The specific fuel consumption increased in volume but had no change in mass. Under the load characteristics, the NOx, CO and THC were reduced by 13%, 55% and 55%, respectively. During the ESC cycle test, the NOx, CO, THC and PM were reduced by 5.2%, 19.3%, 19.8% and 33%, respectively.