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Thanks to the unique features, deployment of battery energy storage systems in distribution systems is ever-increased. Therefore, new models are needed to capture the real-life characteristics. Beside active power, the battery energy storage system can exchange reactive power with the grid due to the inverter-based connection. Although some previous works have considered this issue, a detailed linear model suitable for the realistic large scale distribution systems is not addressed adequately. In this context, this paper proposes a mixed integer linear programming model for optimal battery energy storage system operation in distribution networks. The proposed model considers various parts of the battery energy storage system including battery pack, inverter, and transformer in addition to linear modeling of the reactive power and apparent power flow limit. Moreover, a linear power flow model is used to calculate voltage magnitudes and power losses with high accuracy. The proposed model is applied to the IEEE 33-bus test case and the results prove the accuracy and efficiency of the proposed model. The results demonstrate that considering reactive capability of the batteries offers new benefits including voltage profile improvement, decreasing reactive power flow in the network, reducing network losses, and releasing network and substation capacity. - 2019 Elsevier Ltd Scopus

Accurate power prediction for a battery pack is of great importance for efficient and safe operation of electric vehicles. However, a battery pack typically consists of dozens of cells connected in series, and how to accurately predict its peak power in practical applications has always been a challenging issue. This paper proposes a low-complexity peak power prediction method for a series-connected battery pack, where the peak power of battery pack is predicted depending only on representative cells. Firstly, considering state of charge and voltage limits for power prediction, the representative cells are selected by using two easily available variables, namely characteristic voltage and ohmic resistance. Secondly, the state of charge and voltage of the representative cell are estimated accurately by the dual adaptive extended Kalman filter algorithm. Finally, the multi-parameter limited method for battery pack is developed to predict the peak power under Urban Dynamometer Driving Schedule test at different temperatures. The experimental results verify the feasibility and robustness of the proposed selection method, and the peak power of battery pack is predicted with low complexity and high accuracy.

The studies on an integrated approach for the battery (cell level), battery pack (system level) and battery pack enclosure (system level) are nascent till date. The present work proposes a combined approach for studying the coupled electrochemical-mechanical performance of the battery (cell level), battery pack and enclosure (system level). In this context, five research problems are undertaken involving the design parameters for the performance evaluation of batteries. The first issue undertaken is battery performance under stress conditions and for that varying initial stress and real-time stresses were applied, and variation in the capacity of the battery packs was studied. Initial stress was found to be in negative relation with the battery capacity. For understanding the mechanical damage (leakage, deformation, etc.), characterization of the battery cell under impact loading was done by subjecting battery cells to different impact loads and impact analysis was done using six statistical features extracted from the impact force processed signal of each test, namely: peak value, mean value, variance, skewness, kurtosis and pulse width. The third issue regards the manufacturing defects in the cell, which can potentially lead to inconsistency of cells performance in a pack and thus lower its total cycle life. Support vector classification clustering methods were applied to form a battery pack with similar performing cells and performance of the pack was compared with industrial packs in which earlier one was found to be better. The fourth research challenge involved the mechanical performance parameters such as strength, deformation, and natural frequency, which, in addition to temperature, are also critical to battery pack safety in the event of impact and vibrations under driving conditions. ANSIS and NSGA-II based approaches were used to optimize the battery design and resultant device shown maximum deformation reduced by 22.2%, the value of minimum inherent frequency improved by 3.2% and the value of mass decreased by 11.6%. The fifth research problem highlighted how, due to the enormous production of electric vehicles, by 2020, there will be 250,000 tons of batteries that need to be recycled. A combined experimental-numerical framework has been thereby suggested to mitigate it. The study resulted in the formation of an online monitoring system and safety design of batteries at both cell and system level (pack and enclosure).

Traction batteries are a key factor in the environmental sustainability of electric mobility and, therefore, it is necessary to evaluate their environmental performance to allow a comprehensive sustainability assessment of electric mobility. This article presents an environmental assessment of a lithium-ion traction battery for plug-in hybrid electric vehicles, characterized by a composite cathode material of lithium manganese oxide (LiMn2O4) and lithium nickel manganese cobalt oxide Li(NixCoyMn1-x-y)O2. Composite cathode material is an emerging technology that promises to combine the merits of several active materials into a hybrid electrode to optimize performance and reduce costs. In this study, the environmental assessment of one battery pack (with a nominal capacity of 11.4 kWh able to be used for about 140,000 km of driving) is carried out by using the Life Cycle Assessment methodology consistent with ISO 14040. The system boundaries are the battery production, the operation phase and recycling at the end of life, including the recovery of various material fractions. The composite cathode technology examined besides a good compromise between the higher and the lower performance of NMC and LMO cathodes, can present good environmental performances. The results of the analysis show that the manufacturing phase is relevant to all assessed impact categories (contribution higher than 60%). With regard to electricity losses due to battery efficiency and battery transport, the contribution to the use phase impact of battery efficiency is larger than that of battery transport. Recycling the battery pack contributes less than 11% to all of the assessed impact categories, with the exception of freshwater ecotoxicity (60% of the life cycle impact). The environmental credits related to the recovery of valuable materials (e.g. cobalt and nickel sulphates) and other metal fractions (e.g. aluminium and steel) are particularly relevant to impact categories such as marine eutrophication, human toxicity and abiotic resource depletion. The main innovations of this article are that (1) it presents the first bill of materials of a lithium-ion battery cell for plug-in hybrid electric vehicles with a composite cathode active material; (2) it describes one of the first applications of the life cycle assessment to a lithium-ion battery pack for plug-in hybrid electric vehicles with a composite cathode active material with the aim of identifying the “hot spots” of this technology and providing useful information to battery manufacturers on potentially improving its environmental sustainability; (3) it evaluates the impacts associated with the use phase based on primary data about the battery pack's lifetime, in terms of kilometres driven; and (4) it models the end-of-life phase of the battery components through processes specifically created for or adapted to the case study.

A wavelet transform (WT)-based energy management strategy (EMS) is developed to reduce the damages caused by transient and peak power demands on batteries in plug-in hybrid electric vehicles. A hybrid energy storage system (HESS) consisting of a battery pack, an ultracapacitor pack and two DC/DC converters is established based on MATLAB/Simulink. The WT-based EMS with different decomposition levels is evaluated by using simulation under the New European Driving Cycle (NEDC). Comparison results show that the 3-decomposition-level based EMS is the optimal selection. The developed EMS is further evaluated by using simulation under three typical driving cycles including HWFET, WVUBUS and MANHATTAN. To validate the feasibility of the developed EMS, a hardware-in-the loop (HIL) test bench is constructed to simulate the EMS. The results indicate that the developed WT-based EMS with 3 decomposition levels achieves better accuracy performances.

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

The development of electric vehicles (EVs) is beneficial to a cleaner environment. As the powertrain of EVs, power battery is limited obviously by its fast operating temperature rise and large temperature variation among different parts, so an efficient battery thermal management (BTM) system is desirable. In this paper, a battery thermal management system using kaolin/expanded graphite (EG)/paraffin composite was designed. By contrasting different thermal properties of ternary composites with various proportions, the most suitable composite with 10 wt% EG and 10 wt% kaolin was selected for the thermal management testing, which has a conductivity of larger than 6 W/(m·K) and did not shows any visible leakage after 30 min at 60 °C. In the thermal management test, the temperature of a single battery was effectively controlled under 45 °C even at the discharge rate of 4C, and the temperature difference of the single battery was restrained within 5 °C. Besides, the composite had reduced the temperature of the battery pack by 25.77% at 4C rate, and the temperature difference of the battery pack or each battery in the pack could be greatly controlled once the phase change occurred. In conclusion, the excellent temperature controlling performance of the new phase change material (PCM) was verified in this paper, and the importance of reasonable dosage distribution of PCM in battery pack was put forward.

This paper proposed a Fuel Cell Electric Vehicle (FCEV) power system model which was validated by Hardware-in-the-Loop (HIL) real-time emulation. Multi-stack Fuel Cell (MFC) system was used to increase the system redundancy. Nickel-metal Hydride (NiMH) battery pack (BAT) was selected as the secondary energy source. Dual-input single-output Boost converters were connected with MFC system to satisfy the voltage requirement of DC bus. Bi-directional Buck-Boost converter was connected with BAT and then both discharging and charging modes were controlled. Close loop control strategies were developed for converters to assure their stabilities. Rule-based Energy Management Strategy (REMS) was proposed to distribute the required power among Proton Membrane Exchange Fuel Cells (PEMFCs) and BAT. The proposed model was evaluated by HIL real-time emulation, using the Supplemental Federal Test Procedure US06 (SFTP-US06) driving cycle. MicroLabBox, in which real-time processor and Field Programmable Gate Array (FPGA) were embedded, was selected as the HIL real-time validation platform. The MFC system, BAT model, REMS and low-level controllers were executed by real-time processor while the converter general models were built in FPGA. According to the results, the proposed model and method effectively driven MFC system operating in high efficiency region and successfully kept BAT State of Charge (SOC) in the desired range.

The aerial lithium-ion battery pack works differently from the usual battery packs, the working characteristic of which is intermittent supplement charge and instantaneous large current discharge. An adaptive state of charge estimation method combined with the output voltage tracking strategy is proposed by using the reduced particle - unscented Kalman filter, which is based on the reaction mechanism and experimental characteristic analysis. The improved splice equivalent circuit model is constructed together with its state-space description, in which the operating characteristics can be obtained. The relationship function between the open circuit voltage and the state of charge is analyzed and especially optimized. The feasibility and accuracy characteristics are tested by using the aerial lithium-ion battery pack experimental samples with seven series-connected battery cells. Experimental results show that the state of charge estimation error is less than 2.00%. The proposed method achieves the state of charge estimation accurately for the aerial lithium-ion battery pack, which provides a core avenue for its high-power supply security.

This paper aims at studying the energy management of the dual-source electric bus. Based on the characteristics of the power demand and the energy storage system of the dual-source electric bus, a rule-based control strategy based on wavelet transform is proposed to allocate the energy flow among the energy units to obtain efficient energy transfer. The high and low frequency component power demand are decomposed and reconstructed by wavelet transform theory. The high frequency component power is assigned to the super capacitor, and the low frequency one is assigned to the battery pack or power grid, which can obviously reduce the damage, caused by the power rapid change and surge load, to the battery pack and power grid. The effectiveness of the proposed method is evaluated by the benchmark solved by dynamic programming under both a real driving cycle and the standard China bus driving cycle. Simulation experiments are carried out and the results show that the average power consumption is 0.9335 kWh/km, which has only a difference of 8.25% compared with the benchmark under a real driving cycle. In addition, a 9.02% difference is realized under the standard China bus driving cycle. Finally, the hardware-in-loop experiment is performed and the results show that the proposed strategy is reasonable.

Lithium-ion battery packs have become increasingly important for power supply applications, in which the state of charge estimation and output voltage tracking should be very critical for the safety protection. A novel real-time estimation method is proposed by using the improved extended Kalman filtering algorithm together with the two-order resistance and capacitance circuit network battery model, aiming to solve its security protection issues. Experimental results show that this method can track the voltage signals effectively along with the real-time state estimation in the discharging and charging maintenance operation processes. The battery cell voltage detection accuracy is found to be 1.00 mV and the pack voltage measurement error is less than 20.00 mV. Meanwhile, the state of charge value can be estimated with a great accuracy of 2.00%, in which the state of balance parameter is considered for the internal connected battery cells. The developed experimental associated battery management system can be used for the working state monitoring in the aerial power supply application of the lithium-ion battery pack.

To reuse lithium-ion battery scrapped from electric vehicles in accumulating off-peak electricity at night and unstable renewable energies is regarded as an effective way of extracting the residual capacity in scrapped automotive battery pack. However, decay of life cycle in such reused lithium-ion battery is an issue. In this study, a simple converterless energy management system is developed for controlling the power flow. One physical battery of ultracapacitor is used to couple with energy management system. It functions in the supply of peak power so that to ease the deviation of voltage drop. Two types of lithium-ion batteries (LiFePO4 and LiMnNiCoO2) in electric vehicles are utilized and imposed US federal driving pattern for simulating random use in urban area. Assessment of energy management system is conducted through the bench test of monitoring the voltage drop of battery pack. In addition, a real-time simulator is developed for optimizing current sharing ratio between battery pack and ultracapacitor for minimizing voltage drop, and then compared with bench-test results. In conclusion, energy management system improves up to 50% reduction of voltage drop in the case of LiMnNiCoO2, and much effective than passive control. A rough estimation by calculating the relative effect of elongating life cycle is up to 145% than original usage without energy management system. This study enables us to confirm the performance of converterless energy management system used in scrapped lithium-ion battery pack for reusing in smart-grid energy balancing.

Secondary battery utilization is one of the most promising strategies to solve the problem of battery recycling in the future. The objective of this research is to provide practical solutions for the screening and regrouping of retired lithium batteries. Firstly, a systematic clustering method is proposed. The method is mainly divided into three stages: (1) Fast screening technology of voltage and internal resistance (2) Retired battery status of health (SOH) detection (3) Retired battery clustering method based on self-organizing maps (SOM) neural network. Secondly, a validation experiment was performed. This experiment covers the collection, disassembly of retired battery packs, retired batteries SOH detection, classification, and reassembly of new reuse battery packs. Results show that our proposed screening scheme can quickly identify the initial state of retired batteries and provide a solid basis for further decision-making. After battery test and intelligent SOM screening, the inconsistency of capacity and internal resistance of retired battery pack has been reduced. In addition, the experimental results show that the capacity and potential cycle numbers of reuse pack manufactured by SOM clustering are 25% and 50% more than those of reuse pack manufactured by randomly selected retired batteries. Thus, it proves that the proposed screening method was efficient for retired battery second use. Moreover, the original consistency of retired battery pack has significant impacts on batteries reuse. Thus, the reuse strategies should consider applying for spent battery packs which already has maintained some level of reasonable consistency.

The critical challenges to enlarge the market share of electric vehicles (EVs) are cost, performance, reliability and safety. These issues are closely linked to the energy storage system in the EVs. Lithium-ion batteries have revolutionized the EV industry to become the preferred battery choice for EVs. This is due to their outstanding characteristics including high energy density, high voltage, low self-discharge rate, long cycle life, high charging and discharging rate capability. In EVs, Lithium-ion cells are connected in series and/or parallel to deliver the required power to the traction motor and auxiliary systems. However, due to the operating environment of EVs, the Lithium-ion battery may not perform to the best of its ability when integrated into an EV battery pack. In this work, the integration of Lithium-ion battery into an EV battery pack is investigated from different aspects, namely different battery chemistry, cell packaging, electric connection and control, thermal management, assembly and service and maintenance. In addition, benchmarking study using different cell packaging of Lithium Iron Phosphate cell as an energy storage system is conducted. This study provides a basic guideline for cell selection and integration of cell for the EVs battery pack.

As a large number of lithium-ion batteries are retired from electric vehicles, their reuse is receiving more and more attention. However, a retired battery pack is not suitable for direct reuse due to the poor consistency of in-pack cells. In this paper, we propose an efficient screening method for retired cells based on support vector machine. Firstly, twelve retired LiFePO4 battery modules are dissembled into 240 cells, and their capacity and resistance are measured and analyzed. Secondly, to improve screening efficiency for retired cells, an incremental capacity curve based on high charging current rate is used to rapidly extract their capacity feature and internal resistance. Subsequently, the multi-class model based on support vector machine is trained to classify the retired cells with good consistency. Finally, the retired cells are accurately divided into four classes by the trained model, and the classification accuracy can reach 96.8%. Compared with the traditional method, the time of feature extraction can be reduced by four fifths, and the screening efficiency is greatly improved. Additionally, a current test system is designed to compare the current differences in the new battery module regrouped in parallel by the screened cells. The experimental results show the current consistency is significantly improved compared to that in the original battery module, and the mean of standard deviation used to describe the current inconsistency drops by up to about 14 times.

Internal short circuit is one of the unsolved safety problems that may trigger the thermal runaway of lithium-ion batteries. This paper aims to detect the internal short circuit that occurs in battery pack with parallel-series hybrid connections based on the symmetrical loop circuit topology. The theory of the symmetrical loop circuit topology answers the question that: 1) How to locate an exact internal short circuit fault in a battery pack with hybrid electric connections. 2) What kind of signal is needed for detecting the fault; 3) The minimum requirement of using additional Ampere Meters for fault diagnosis. The Ampere Meters extract new signal that reflects the imbalanced current among the parallel-connected cells. The internal short circuit is judged according to the number theory and the circuit topology. Algorithm using recursive least square locks the fault cell online. Substitute internal short circuit experiments validate the proposed algorithm at pack level. The proposed approach detects the fault of internal short circuit efficiently and accurately, having great potential to be applied in the fault diagnosis of battery pack for large scale energy storage systems.

Remaining useful life estimation is of great importance to customers who use battery-powered products. This paper develops a remaining useful life estimation model based on k-nearest neighbor regression by incorporating data from all the cells in a battery pack. A differential evolution technique is employed to optimize the parameters in the estimation model. In this approach, remaining useful life is estimated from a weighted average of the useful life of several nearest cells that share a similar degradation trend to the cell whose remaining useful life needs to be estimated. The developed method obtains a remaining useful life estimation result with average error of 9 cycles, and the best estimation only has an error of 2 cycles. All of these estimations are done within 10 ms. Increasing the number of tested cells and nearest cells improves the estimation accuracy. The developed method reduces the estimation average error by 83.14% and 89.79% compared to particle filter and support vector regression, respectively. Therefore, estimation results and comparison validate the effectiveness of the developed method for remaining useful life estimation of lithium-ion cells.