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Developing new battery configurations is a time‐consuming process, electrochemical models can be employed to expedite the process by predicting the performance of battery designs. An electrochemical pseudo‐two‐dimensional modeling framework is created for both LNMO|LiPF6 in EC/PC/sulfolane|Gr and LNMO|PVdF GPE with 1.15 M LiPF6 in EC/PC/FEC/sulfolane|Gr configurations with gelifiable electrodes, leveraging the experimental characterization of battery components for enhanced accuracy. Parametrization experiments include quasi‐open circuit potential, galvanostatic intermittent titration technique, electrochemical impedance spectroscopy, microscopy and the four‐point resistance method. The modeling framework is initiated at full coin cell level and subsequently extended to encompass monolayered, 200 mAh and 500 mAh multilayered pouch cells. Experimental validation, through voltage‐capacity measurements, ensures the accuracy of the models. The discharge curves indicate that cells incorporating GPE show similar performance with liquid electrolytes. The impact of increasing current rates on performance is examined by scrutinizing the polarization trends during discharge and the Li‐ion concentration in the electrolyte phase. Elevated C‐rates induce heightened polarization, detrimentally influencing performance. Importantly, the developed model for full coin cells can be seamlessly expanded to encompass 500 mAh multilayered pouch cells without requiring additional component characterization. This versatility allows the modeling framework to be a valuable tool for evaluating the performance of battery configurations at higher levels. [ABSTRACT FROM AUTHOR]

In this study, we present an innovative, fully automated, and digitalized methodology to optimize the energy efficiency and cost effectiveness of Li-ion battery modules. Advancing on from today's optimization schemes that rely on user experience and other limitations, the mechanical and thermal designs are optimized simultaneously in this study by coupling 3D multi-physical behavior models to multi-objective heuristic optimization algorithms. Heat generation at various loading and ambient conditions are estimated with a physics-based, fractional-order battery cell-level model, which is extrapolated to a module that further accounts for the interconnected cells' heat transfer phenomena. Several key performance indicators such as the surface temperature increase, the temperature variations on the cells, and heat uniformity within the module are recorded. For the air-cooled study case, the proposed coupled framework performs more than 250 module evaluations in a relatively short time for the whole available electro-thermal-mechanical design space, thereby ensuring global optimal results in the final design. The optimal module design proposed by this method is built in this work, and it is experimentally evaluated with a module composed of 12 series-connected Li-ion NMC/C 43Ah prismatic battery cells. The performance is validated at various conditions, which is achieved by accounting the thermal efficiency and pressure drop with regard to power consumption improvements. The validations presented in this study verify the applicability and overall efficiency of the proposed methodology, as well as paves the way toward better energy and cost-efficient battery systems. [ABSTRACT FROM AUTHOR]

Wind energy is an increasingly important renewable resource in today's global energy landscape. However, it faces challenges due to the unpredictable nature of wind speeds, resulting in intermittent power generation. This intermittency can disrupt power grid stability when integrating doubly fed induction generators (DFIGs). To address this challenge, we propose integrating a Li-ion battery energy storage system (BESS) with the direct current (DC) link of grid-connected DFIGs to mitigate power fluctuations caused by variable wind speed conditions. Our approach entails meticulous battery modeling, sizing, and control methods, all tailored to match the required output power of DFIG wind turbines. To demonstrate how well our Li-ion battery solution works, we have developed a MATLAB/Simulink R2022a version model. This model enables us to compare situations with and without the Li-ion battery in various operating conditions, including steady-state and dynamic transient scenarios. We also designed a buck–boost bidirectional DC-DC converter controlled by a proportional integral controller for battery charging and discharging. The battery actively monitors the DC-link voltage of the DFIG wind turbine and dynamically adjusts its stored energy in response to the voltage level. Thus, DFIG wind turbines consistently generate 1.5 MW of active power, operating with a highly efficient power factor of 1.0, indicating there is no reactive power produced. Our simulation results confirm that Li-ion batteries effectively mitigate power fluctuations in grid-connected DFIG wind turbines. As a result, Li-ion batteries enhance grid power stability and quality by absorbing or releasing power to compensate for variations in wind energy production. [ABSTRACT FROM AUTHOR]

The World Electric Vehicle Journal is a peer-reviewed scientific journal that focuses on studies related to battery, hybrid, and fuel cell electric vehicles. It was established in 2007 and aims to provide a platform for scientific communication in the field of electric vehicles. The journal has recently updated its aims and scope, with the addition of a new topic on manufacturing. The journal covers a wide range of topics, including vehicle and transportation systems, electric autonomous and connected vehicles, charging infrastructure and grid integration, marketing and promotion, storage systems, propulsion systems and components, power electronics components, electric vehicle management, energy supply and sustainability, and manufacturing. [Extracted from the article]

Lithium-ion batteries are currently the pioneers of green transition in the transportation sector. The nickel-manganese-cobalt (NMC) technology, in particular, has the largest market share in electric vehicles (EVs), offering high specific energy, optimized power performance, and lifetime. The aging of different lithium-ion battery technologies has been a major research topic in the last decade, either to study the degradation behavior, identify the associated aging mechanisms, or to develop health prediction models. However, the lab-scale standard test protocols are mostly utilized for aging characterization, which was deemed not useful since batteries are supposed to age dynamically in real life, leading to aging heterogeneity. In this research, a commercial NMC variation (4-4-2) was aged with a pragmatic standard-drive profile to study aging behavior. The characterized measurable parameters were statistically investigated before performing an autopsy on the aged battery. Harvested samples of negative and positive electrodes were analyzed with Scanning Electron Microscopy (SEM) and the localized volumetric percentile of active materials was reported. Loss of lithium inventory was found to be the main aging mechanism linked to 20% faded capacity due to heavy electrolyte loss. Sparsely distributed fluorine from the lithium salt was found in both electrodes as a result of electrolyte decomposition. [ABSTRACT FROM AUTHOR]

The most employed technique to mimic the behavior of lithium-ion cells to monitor and control them is the equivalent circuit model (ECM). This modeling tool should be precise enough to ensure the system's reliability. Two significant parameters that affect the accuracy of the ECM are the applied current rate and operating temperature. Without a thorough understating of the influence of these parameters on the ECM, parameter estimation should be carried out manually within the calibration, which is not favorable. In this work, an enhanced ECM was developed for high-power lithium-ion capacitors (LiC) for a wide temperature range from the freezing temperature of −30 °C to the hot temperature of +60 °C with the applied rates from 10 A to 500 A. In this context, experimental tests were carried out to mimic the behavior of the LiC by modeling an ECM with two RC branches. In these branches, two resistance and capacitance (RC) are required to maintain the precision of the model. The validation results proved that the semi-empirical second-order ECM can estimate the electrical and thermal parameters of the LiC with high accuracy. In this context, when the current rate was less than 150 A, the error of the developed ECM was lower than 3%. Additionally, when the demanded power was high, in current rates above 150 A, the simulation error was lower than 5%. [ABSTRACT FROM AUTHOR]

Lithium-ion (Li-ion) battery cells are influenced by high energy, reliability, and robustness. However, they produce a noticeable amount of heat during the charging and discharging process. This paper presents an optimal thermal management system (TMS) using a phase change material (PCM) and PCM-graphite for a cylindrical Li-ion battery module. The experimental results show that the maximum temperature of the module under natural convection, PCM, and PCM-graphite cooling methods reached 64.38, 40.4, and 39 °C, respectively. It was found that the temperature of the module using PCM and PCM-graphite reduced by 38% and 40%, respectively. The temperature uniformity increased by 60% and 96% using the PCM and PCM-graphite. Moreover, some numerical simulations were solved using COMSOL Multiphysics® for the battery module. [ABSTRACT FROM AUTHOR]

This paper investigates the model development of the state-of-power (SoP) estimation for a 43 Ah large-capacity prismatic nickel-manganese-cobalt oxide (NMC) based lithium-ion cell with a thorough aging investigation of the cells' internal resistance increase. For a safe operation of the vehicle system, a battery management system (BMS) integrated with SoP estimation functions is crucial. In this study, the developed SoP model used for the estimation of power throughout the lifetime of the cell is coupled with a dual-polarization equivalent-circuit model (DP_ECM) for achieving the precise estimation of desired parameters. The SoP model is developed based on the pulse-trained internal resistance evolution approach, and hence the power is estimated by determining the rate of internal resistance increase. Hybrid pulse power characterization (HPPC) test results are used for extraction of the impedance parameters. In the DP_ECM, Coulomb counting and extended Kalman filter (EKF) state estimation methods are developed for the accurate estimation of the state of charge (SoC) of the cell. The SoP model validation is performed by using both dynamic Worldwide harmonized Light vehicles Test Cycles (WLTC) and static current profiles, achieving promising results with root-mean-square errors (RMSE) of 2% and 1%, respectively. [ABSTRACT FROM AUTHOR]

This article presents a flexible controller algorithm for bidirectional modular cascaded H-bridge (CHB)-based battery storage systems. The primary objectives are to maintain high-quality power on the ac side while balancing dc-side battery voltages under charging or discharging operations. The proposed algorithm is based on a finite-set model-predictive current control (FSMPC) technique, which performs in-phase and phase voltage equalization in two complementary balancing terms by taking advantage of intrinsic voltage vector redundancy characteristic of three-phase CHB converters. Additionally, the proposed modifications to the FSMPC provide lower demand on computational resources of the controller through offline reordering of vectors and adjacent vector introduction, which also brings the least differential-mode $dv/dt$ as an added value. Effects of battery voltage variation in charge/discharge cycles and voltage drop caused by nonideal semiconductors are considered to ensure the most accurate performance during steady-state, transient, and common-mode voltage reduction operation. The proposed approach is an efficient easy-to-implement multiparameter controller, which handles all the tasks needless to any additional control loops. Simulations and experimental prototyping results validate the feasibility of the battery voltage balancer terms to be utilized in battery storage applications with no downside impact on key power exchange functions. [ABSTRACT FROM AUTHOR]

The acceleration demand from the driver in electric vehicles (EVs) should be supported by high-power energy storage systems (ESSs). In order to satisfy the driver's request, the employed ESS should have high power densities. On the other hand, high energy densities are required at the same time for EVs' traction to minimize the range anxiety. In this context, a novel ESS has emerged that can provide high power and energy densities at the same time. Such technology is called lithium-ion capacitor (LiC), which employs Li-doped carbon as negative electrode and activated carbon as positive electrode. However, high heat generation in high current applications is an issue that should be managed to extend the LiCs life span. Hence, a proper thermal management system (TMS) is mandatory for such a hybrid technology. Since this ESS is novel, there are only several TMSs addressed for LiCs. In this review article, a literature study regarding the developed TMSs for LiCs is presented. Since LiCs use Li-doped carbon in their negative electrodes, lithium-titanate oxide (LTO) batteries are the most similar lithium-ion batteries (LiBs) to LiCs. Therefore, the proposed TMSs for lithium-ion batteries, especially LTO batteries, have been explained as well. The investigated TMSs are active, passive, and hybrid cooling methods The proposed TMSs have been classified in three different sections, including active methods, passive methods, and hybrid methods. [ABSTRACT FROM AUTHOR]

Activated carbons play an important role in enabling Li-ion batteries for high-power applications. They are well established in the application side of energy storage but remain untouched for integration toward conceptualization. As conceptualization attracts growing interest in the field of energy storage, more work needs to be done to bridge the gap between application and concept. Being a part of closing the gap between experiment and modeling, an electrochemical parameterization of commercial activated carbons having different specifications was investigated within the present study. The apparent Li-ion diffusion coefficient and exchange current density of different activated carbons were calculated. Results confirmed that the Li-ion diffusion coefficient was promoted by the ordered structure. Electronically conductive activated carbons exhibited low charge-transfer resistance and, hence, high exchange current density. Besides, long-term capacity retention is strongly linked with the activated carbons' degree of structural order. According to the simulated discharge capacities extracted from the simplified 1D full-cell model with LiMn2O4-Activated Carbon cell chemistry, a notable improvement was observed in comparison with reference LMO-Graphite systems at all C-rates. Nevertheless, activated carbons proved their significance at especially high rates. When 20C was applied, a strong correlation can be built with the long-term cyclic behavior of activated carbon half-cells. [ABSTRACT FROM AUTHOR]

This review paper aims to provide the background and literature review of a hybrid energy storage system (ESS) called a lithium-ion capacitor (LiC). Since the LiC structure is formed based on the anode of lithium-ion batteries (LiB) and cathode of electric double-layer capacitors (EDLCs), a short overview of LiBs and EDLCs is presented following the motivation of hybrid ESSs. Then, the used materials in LiC technology are elaborated. Later, a discussion regarding the current knowledge and recent development related to electro-thermal and lifetime modeling for the LiCs is given. As the performance and lifetime of LiCs highly depends on the operating temperature, heat transfer modeling and heat generation mechanisms of the LiC technology have been introduced, and the published papers considering the thermal management of LiCs have been listed and discussed. In the last section, the applications of LiCs have been elaborated. [ABSTRACT FROM AUTHOR]

Featured Application: The research outcome would serve as a guideline for developing the comprehensive battery pack lifetime model from cell-level validated models. The proposed framework can be adopted in the battery management system with the potential to enhance performance, lifetime, reliability, and safety. The automotive energy storage market is currently dominated by the existing Li-ion technologies that are likely to continue in the future. Thus, the on-road electric (and hybrid) vehicles running on the Li-ion battery systems require critical diagnosis considering crucial battery aging. This work aims to provide a guideline for pack-level lifetime model development that could facilitate battery maintenance, ensuring a safe and reliable operational lifespan. The first of the twofold approach is a cell-level empirical lifetime model that is developed from a lab-level aging dataset of commercial LTO cells. The model is validated with an exhaustive sub-urban realistic driving cycle yielding a root-mean-square error of 0.45. The model is then extended to a 144S1P modular architecture for pack-level simulation. The second step provides the pack electro-thermal simulation results that are upscaled from a cell-level and validated 1D electrical model coupled with a 3D thermal model. The combined simulation framework is online applicable and considers the relevant aspects into account in predicting the battery system's lifetime that results in over 350,000 km of suburban driving. This robust tool is a collaborative research outcome from two Horizon2020 EU projects—GHOST and Vision xEV, showcasing outstanding cell-level battery modeling accuracies. [ABSTRACT FROM AUTHOR]

Lithium-ion capacitors (LiC) are hybrid energy storage systems (ESS) combining the advantages of lithium-ion batteries and electric double-layer capacitors, including longer lifetime, high power, and energy densities. LiCs are popular for high-power applications where fast charge and discharge driving profiles are demanded from electric vehicles (EV). However, LiCs generate excess heat when they are exposed to fast charging/discharging profiles. Therefore, a robust thermal management system (TMS) is crucial, in order to ensure reliable operation. In this study, a novel hybrid TMS based on air-cooling system assisted phase change materials (PCM), heat pipes, and a heat sink is proposed for an LiC module under a 150 A continuous current profile. A very thin aluminum heat sink and flat copper heat pipes were added to the PCM to increase its thermal conductivity. An experimental test bench of the proposed TMS was developed, and the temperature distribution of the module for each of the individual LiC cells was studied. The maximum temperature of the module under natural convection, when there was not any cooling system, reached almost 59.8 °C. The experimental results showed that after using the proposed hybrid TMS, the hottest cell reached 36.18 °C while the coldest cell reached 35.54 °C. Therefore, 39.5% improvement could be seen during the whole charge and discharge process after 3000 s. Moreover, the temperature difference within the module, of four LiCs, was around 0.64 °C, which was exceptional. [ABSTRACT FROM AUTHOR]

High-power lithium-ion capacitors (LiC) are hybrid energy storage systems (EES) with the combined benefits of lithium-ion batteries (LiB) and supercapacitors, such as high specific energy, high specific power, and a long lifetime. Such advanced technology can be used in high-power applications when high charging and discharging are demanded. Nevertheless, their performance and lifetime highly depend on temperature. In this context, this paper presents an optimal passive thermal management system (TMS) employing phase-change materials (PCM) combined with graphite to maintain the LiC maximum temperature. To evaluate the thermal response of the PCM and the PCM/G, experimental tests have been performed. The results exhibit that when the cell is under natural convection, the maximum temperature exceeds 55 °C, which is very harmful for the cell's lifetime. Using the pure paraffin PCM, the maximum temperature of the LiC was reduced from 55.3 °C to 40.2 °C, which shows a 27.3% temperature reduction compared to natural convection. Using the PCM/G composite, the maximum temperature was reduced from 55.3 °C (natural convection) to 38.5 °C, a 30.4% temperature reduction compared to natural convection. The main reason for this temperature reduction is the PCM's high latent heat fusion, as well as the graphite thermal conductivity. Moreover, different PCM/G thicknesses were investigated for which the maximum temperature of the LiC reached 38.02 °C, 38.57 °C, 41.18 °C, 43.61 °C, and 46.98 °C for the thicknesses of 15 mm, 10 mm, 7 mm, 5 mm, and 2 mm, respectively. In this context, a thickness of 10 mm is the optimum thickness to reduce the cost, weight, volume, and temperature. [ABSTRACT FROM AUTHOR]

Lithium-ion capacitor technology (LiC) is well known for its higher power density compared to electric double-layer capacitors (EDLCs) and higher energy density compared to lithium-ion batteries (LiBs). However, the LiC technology is affected by a high heat generation problem in high-power applications when it is continuously being charged/discharged with high current rates. Such a problem is associated with safety and reliability issues that affect the lifetime of the cell. Therefore, for high-power applications, a robust thermal management system (TMS) is essential to control the temperature evolution of LiCs to ensure safe operation. In this regard, developing accurate electrical and thermal models is vital to design a proper TMS. This work presents a detailed 1D/3D electro-thermal model at module level employing MATLAB/SIMULINK® coupled to the COMSOL Multiphysics® software package. The effect of the inlet coolant flow rate, inlet coolant temperature, inlet and outlet positions, and the number of arcs are examined under the cycling profile of a continuous 150 A current rate without a rest period for 1400 s. The results prove that the optimal scenario for the LCTMS would be the inlet coolant flow rate of 500 mL/min, the inlet temperature of 30 ℃, three inlets, three outlets, and three arcs in the coolant path. This scenario decreases the module's maximum temperature (Tmax) and temperature difference by 11.5% and 79.1%, respectively. Moreover, the electro-thermal model shows -5% and -4% errors for the electrical and thermal models, respectively. [ABSTRACT FROM AUTHOR]

Coating conducting polymers onto active cathode materials has been proven to mitigate issues at high current densities stemming from the limited conducting abilities of the metal-oxides. In the present study, a carbon coating was applied onto nickel-rich NMC622 via polymerisation of furfuryl alcohol, followed by calcination, for the first time. The formation of a uniform amorphous carbon layer was observed with scanning- and transmission-electron microscopy (SEM and TEM) and X-ray photoelectron spectroscopy (XPS). The stability of the coated active material was confirmed and the electrochemical behaviour as well as the cycling stability was evaluated. The impact of the heat treatment on the electrochemical performance was studied systematically and was shown to improve cycling and high current performance alike. In-depth investigations of polymer coated samples show that the improved performance can be correlated with the calcination temperatures. In particular, a heat treatment at 400 °C leads to enhanced reversibility and capacity retention even after 400 cycles. At 10C, the discharge capacity for carbon coated NMC increases by nearly 50% compared to uncoated samples. This study clearly shows for the first time the synergetic effects of a furfuryl polymer coating and subsequent calcination leading to improved electrochemical performance of nickel-rich NMC622. [ABSTRACT FROM AUTHOR]

This article attempts to study the thermal characteristic and behavior of a large commercial prismatic lithium-ion battery cell (50 Ah) for the application of rechargeable energy storage in electric vehicles. The anode of the cell is based on graphite, whereas nickel, cobalt, and manganese make up the cathode of the cell. With the experiments, two different load protocols were applied to the cell in three initial ambient temperature conditions. With the help of thermal sensors and a thermal camera, the surface temperature of the cell is recorded. Moreover, temperature growth and distribution were compared with the variable loads and initial temperature conditions. Furthermore, a three-dimensional thermal model is built and the results are validated with the experiments. A maximum of around 2 °C temperature fluctuation is recorded. Finally, it was concluded that temperature distribution is affected by the load profile in different ambient conditions. [ABSTRACT FROM AUTHOR]

Lithium‐ion technologies have become the most attractive and selected choice for battery electric vehicles. However, the understanding of battery aging is still a complex and nonlinear experience which is critical to the modeling methodologies. In this work, a comprehensive lifetime modeling twin framework following semi‐empirical methodology has been developed to predict the crucial degradation outputs accurately in terms of capacity fade and resistance increase. The constructed model considers all the relevant aging influential factors for commercial nickel manganese cobalt (NMC) Li‐ion cells based on long‐term laboratory‐level investigation and combines both the cycle life and the calendar life aspects. To demonstrate robustness, the model is validated with a real‐life worldwide harmonized light‐duty test cycle (WLTC). The model can precisely predict the capacity fade and the internal resistance growth with a root‐mean‐squared error (RMSE) of 1.31% and 0.56%, respectively. The developed model can be used as an advanced online tool forecasting the lifetime based on dynamic profiles. [ABSTRACT FROM AUTHOR]

This research demonstrates modular battery storage systems' voltage balancing using cascaded H‐bridge (CHB) converters. The main principle is to maintain AC‐side high‐quality power absorption or injection, while on the DC‐side independent battery units' voltages get balanced. The CHB converter operates in inversion or rectification modes to discharge the storage system to an AC load/grid, or to charge it from an AC source, respectively. To achieve this goal, several challenges as high‐differential mode dv/dt, and dealing with semiconductors voltage drop also arise which should get tackled through feasible controller design. Model predictive control (MPC), as a flexible technique capable of multi‐parameter controlling, is used to achieve the beforementioned system objectives in a simple, efficient, and scalable manner. The proposed MPC‐based algorithm is an AC current controller, which handles DC‐side voltage equalisation and introduces adjacent CHB voltage levels to reduce dv/dt and performs switches voltage loss modelling all in a single control block. An additional linear controller based on multi‐carrier pulse width modulation is also implemented for comparison with the MPC‐based design. Experimental prototyping and simulation results validate the feasibility of this approach. [ABSTRACT FROM AUTHOR]

This paper presents an integrated model for energy consumption and range estimation, capable of energy-efficient routing. This integrated model predicts the energy consumption on all road segments in the road network and applies shortest path algorithms to calculate energy-efficient routes. A temperature-dependent model of the battery internal resistance based on real-world data translates the energy consumption prediction into driving range. The graph representation of the road network is transformed from a node-based graph to an edge-based graph to allow cost allocation of one edge based on the characteristics of the preceding edge. The integrated model is used to perform an analysis of energy-efficient routes for a real-life scenario. The analysis showed that the energy-optimal route is different from the time- and distance-optimal route with energy-efficiency gains up to 37% for the chosen scenario. The energy-efficient route tends to lean toward a distance-optimal route in the case of low auxiliary consumption and to lean toward a time-optimal route in the case of high auxiliary consumption. The energy-efficient route generation is tested in real life for one case of the chosen scenario. The measured results showed a good match with the predicted values for the energy consumption with a 9% mean relative error and preserved the ranking of all routes in terms of the travel distance, travel time, and energy consumption. The proposed integrated model is a functional model for energy consumption in real-life that differentiates many energy consumption influencing factors and produces energy-efficient routes with good accuracy. [ABSTRACT FROM AUTHOR]

The energy management strategy (EMS) or power management strategy (PMS) unit is the core of power sharing control in the hybridization of automotive drivetrains in hybrid electric vehicles (HEVs). Once a new topology and its corresponding EMS are virtually designed, they require undertaking different stages of experimental verifications toward guaranteeing their real-world applicability. The present paper focuses on a new and less-extensively studied topology of such vehicles, HEVs equipped with an electrical variable transmission (EVT) and assessed the controllability validation through hardware-in-the-loop (HiL) implementations versus model-in-the-loop (MiL) simulations. To this end, first, the corresponding modeling of the vehicle components in the presence of optimized control strategies were performed to obtain the MiL simulation results. Subsequently, an innovative versatile HiL test bench including real prototyped components of the topology was introduced and the corresponding experimental implementations were performed. The results obtained from the MiL and HiL examinations were analyzed and statistically compared for a full input driving cycle. The verification results indicate robust and accurate actuation of the components using the applied EMSs under real-time test conditions. [ABSTRACT FROM AUTHOR]

Lithium-ion capacitors (LiCs) have emerged as a promising technology for automotive applications due to the solution offered by their power density, high voltage operation and their excellent durability (more than 2 million cycles). Nevertheless, the reliability of LiCs can be drastically affected by overheating issues which raise the importance of thermal management. Nowadays, active cooling is employed to keep the battery systems temperature in range. However, due to the bulkiness and weight of the existing active cooling systems, latent heat thermal energy storage through the use of phase change materials (PCMs), represents an attractive way. In this paper, paraffin is investigated experimentally and simulated as a PCM cooling solution for the thermal management of a dual-LiC module. Moreover, since paraffin presents a low thermal conductivity characteristic, an additional component such as aluminium mesh grid is inserted to the system to solve the low conductivity issue. Results show good performance of the Al-PCM on the battery thermal management for both ESSs with a relatively lower temperature. The Al-PCM combination was able is lower the temperature to 36 °C compared to natural convection (46 °C). [ABSTRACT FROM AUTHOR]

The prognosis and health management of lithium-ion batteries are extremely important issues for operating performance as well as the cost of energy storage systems in vehicular applications. This is achieved through the estimation of the state-of-health and the prediction of remaining useful life (RUL). This paper presents a lithium battery prognosis model considering the battery-aging conditions. The proposed model is developed based on the Rao-Blackwellization particle filter, which is able to estimate the posterior values of the aging indicators, i.e., capacity and resistance, and to predict the RUL. The particularity of the proposed model is that it considers the aging conditions of batteries as inputs of the prognosis model. In order to validate the proposed method, experiments have been carried out under different aging conditions for three types of lithium-ion batteries. The proposed model performances have been evaluated. A comparison against the particle filter prognosis model is presented. Results highlight the effectiveness of the proposed technique to predict the RUL for different cases: initial conditions, types of lithium-ion batteries, and aging conditions. The RUL prediction using the proposed prognosis model presents a maximum relative error of 6.64%, which is low compared to 14.3% when a simple particle filter prognosis model is used. [ABSTRACT FROM AUTHOR]

This paper proposes an optimal design for hybrid grid-connected Photovoltaic (PV) Battery Energy Storage Systems (BESSs). A smart grid consisting of PV generation units, stationary Energy Storage Systems (ESSs), and domestic loads develops a multi-objective optimization algorithm. The optimization aims at minimizing the Total Cost of Ownership (TCO) and the Voltage Deviation (VD) while considering the direct and indirect costs for the prosumer, and the system stability with regard to intermittent PV generation. The optimal solution for the optimization of the PV-battery system sizing with regard to economic viability and the stability of operation is found while using the Genetic Algorithm (GA) with the Pareto front. In addition, a fuzzy logic-based controller is developed to schedule the charging and discharging of batteries while considering the technical and economic aspects, such as battery State of Charge (SoC), voltage profile, and on/off-peak times to shave the consumption peaks. Thus, a hybrid approach that combines a Fuzzy Logic Controller (FLC) and the GA is developed for the optimal sizing of the combined Renewable Energy Sources (RESs) and ESSs, resulting in reductions of approximately 4% and 17% for the TCO and the VD, respectively. Furthermore, a sensitivity cost-effectiveness analysis of the complete system is conducted to highlight and assess the profitability and the high dependency of the optimal system configuration on battery prices. [ABSTRACT FROM AUTHOR]

Lithium-ion battery packs inside electric vehicles represents a high share of the final price. Nevertheless, with technology advances and the growth of the market, the price of the battery is getting more competitive. The greenhouse gas emissions and the battery cost have been studied previously, but coherent boundaries between environmental and economic assessments are needed to assess the eco-efficiency of batteries. In this research, a detailed study is presented, providing an environmental and economic assessment of the manufacturing of one specific lithium-ion battery chemistry. The relevance of parameters is pointed out, including the manufacturing place, the production volume, the commodity prices, and the energy density. The inventory is obtained by dismantling commercial cells. The correlation between the battery cost and the commodity price is much lower than the correlation between the battery cost and the production volume. The developed life cycle assessment concludes that the electricity mix that is used to power the battery factory is a key parameter for the impact of the battery manufacturing on climate change. To improve the battery manufacturing eco-efficiency, a high production capacity and an electricity mix with low carbon intensity are suggested. Optimizing the process by reducing the electricity consumption during the manufacturing is also suggested, and combined with higher pack energy density, the impact on climate change of the pack manufacturing is as low as 39.5 kg CO2 eq/kWh. [ABSTRACT FROM AUTHOR]

Characterizing lithium-ion batteries is of prime importance as it helps in understanding the safety, working temperature, voltage range, and power capabilities. Based on these results, we can then choose operating conditions which include safety protocols, application, and working environment. In this study, EIS studies of commercially available 20-Ah lithium-ion battery and a 28-Ah prototype cell with nickel manganese cobalt oxide (NMC)/graphite chemistry are used to determine the contribution of temperature and state of charge (SoC) towards the electrochemical impedance spectroscopy. These cells are manufactured for electric vehicle (EV) application. The electrode structure, particle size, stacking of the electrodes, and other entities for both the cells are provided to compare the similarities and differences between both the cells. Equivalent circuit modeling is used to analyze and comprehend the variation in impedance spectrum obtained for both the cells. It is observed that the ohmic resistance varies with both temperature and SoC and the variation with temperature is more significant for the prototype cell. The prototype cell showed better charge-transfer characteristics at lower temperatures when compared to the commercial cell. [ABSTRACT FROM AUTHOR]

Featured Application: In the long term, results presented with this paper suggest to carry out in a systematic manner, the evaluation of batteries performances at the cycling temperature. According to this, in real applications as in automotive, it can be recommended to use the track of the temperature within the battery pack (e.g., mean) recorded by the battery management system, to define the temperature at which battery cells performances should be evaluated. In addition, room temperature can be used if needed for the comparison of battery cells performances, of same type that are though not operating at the same temperature. This paper presents the results regarding the thermal characterisation and modelling of high energy lithium-ion battery cells at both room (25 °C) and cycling (35 °C) temperatures. In this work two types of Nickel Manganese Cobalt (NMC) batteries are studied: a fresh (or uncycled) and an aged (or cycled) battery cells. The ageing of the studied NMC battery cells is achieved by means of accelerated ageing tests (i.e., repetition of numerous charge and discharge cycles) at 35 °C cycling temperature. Temperature at the surface of the battery cells is characterised, with a set of three discharge current rates 0.3C (i.e., 6 A), 1C (i.e., 20 A) and 2C (i.e., 40 A), and the evolutions at three different locations on the surface of the battery cells namely, at the top, in the center and at the bottom regions are measured. In addition, temperature and ageing dependent electrochemical-thermal modelling of the uncycled and cycled battery cells is also successfully accomplished in case of both room and cycling temperatures. Numerical simulations were carried out in case of high 2C constant current rate, and the assessment of the modelling accuracy by comparison of the predicted battery cells voltage and temperature with respect to the experimental data is further presented. With this paper, thermal performances of battery cells prior and after long-term cycling are evaluated at the cycling temperature, next to the ambient temperature. Hence, thermal characterisation and modelling results are more closely reflecting that encountered by the battery cells in real cycling conditions, so that their performances are believed in this way to be more objectively evaluated. [ABSTRACT FROM AUTHOR]

Life Cycle assessments (LCAs) on electric mobility are providing a plethora of diverging results. 44 articles, published from 2008 to 2018 have been investigated in this review, in order to find the extent and the reason behind this deviation. The first hurdle can be found in the goal definition, followed by the modelling choice, as both are generally incomplete and inconsistent. These gaps influence the choices made in the Life Cycle Inventory (LCI) stage, particularly in regards to the selection of the electricity mix. A statistical regression is made with results available in the literature. It emerges that, despite the wide-ranging scopes and the numerous variables present in the assessments, the electricity mix's carbon intensity can explain 70% of the variability of the results. This encourages a shared framework to drive practitioners in the execution of the assessment and policy makers in the interpretation of the results. [ABSTRACT FROM AUTHOR]

DC/DC Multiport Converters (MPC) are gaining interest in the hybrid electric drivetrains (i.e., vehicles or machines), where multiple sources are combined to enhance their capabilities and performances in terms of efficiency, integrated design and reliability. This hybridization will lead to more complexity and high development/design time. Therefore, a proper design approach is needed to optimize the design of the MPC as well as its performance and to reduce development time. In this research article, a new design methodology based on a Multi-Objective Genetic Algorithm (MOGA) for non-isolated interleaved MPCs is developed to minimize the weight, losses and input current ripples that have a significant impact on the lifetime of the energy sources. The inductor parameters obtained from the optimization framework is verified by the Finite Element Method (FEM) COMSOL software, which shows that inductor weight of optimized design is lower than that of the conventional design. The comparison of input current ripples and losses distribution between optimized and conventional designs are also analyzed in detailed, which validates the perspective of the proposed optimization method, taking into account emerging technologies such as wide bandgap semiconductors (SiC, GaN). [ABSTRACT FROM AUTHOR]

In this article, two techniques that are congruous with the principle of control theory are utilized to estimate the state of health (SOH) of real-life plug-in hybrid electric vehicles (PHEVs) accurately, which is of vital importance to battery management systems. The relation between the battery terminal voltage curve properties and the battery state of health is modelled via an adaptive neuron-fuzzy inference system and a group method of data handling. The comparison of the results demonstrates the capability of the proposed techniques for accurate SOH estimation. Moreover, the estimated results are compared with the direct actual measured SOH indicators using standard tests. The results indicate that the adaptive neuron-fuzzy inference system with fifteen rules based on a SOH estimator has better performances over the other technique, with a 1.5% maximum error in comparison to the experimental data. [ABSTRACT FROM AUTHOR]

Featured Application: A potential application for this research work is the pure electric bus with energy recovery capability. With the hybrid energy storage system based on Lithium-ion battery and Lithium-ion Capacitor, the bus will have a longer range, a higher efficiency and a lower cost in comparison to a bus with non-hybrid energy storage system or a bus with hybrid energy storage based on battery and super-capacitors. Public transportation based on electric vehicles has attracted significant attention in recent years due to the lower overall emissions it generates. However, there are some barriers to further development and commercialization. Fewer charging facilities in comparison to gas stations, limited battery lifetime, and extra costs associated with its replacement present some barriers to achieve better acceptance. A practical solution to improve the battery lifetime and driving range is to eliminate the large-magnitude pulse current flow from and to the battery during acceleration and deceleration. Hybrid energy storage systems which combine high-power (HP) and high-energy (HE) storage units can be used for this purpose. Lithium-ion capacitors (LiC) can be used as a HP storage unit, which is similar to a supercapacitor cell but with a higher rate capability, a higher energy density, and better cyclability. In this design, the LiC can provide the excess power required while the battery fails to do so. Moreover, hybridization enables a downsizing of the overall energy storage system and decreases the total cost as a consequence of lifetime, performance, and efficiency improvement. The aim of this paper is to investigate the effectiveness of the hybrid energy storage system in protecting the battery from damage due to the high-power rates during charging and discharging. The procedure followed and presented in this paper demonstrates the good performance of the evaluated hybrid storage system to reduce the negative consequences of the power peaks associated with urban driving cycles and its ability to improve the lifespan by 16%. [ABSTRACT FROM AUTHOR]

This paper examines the impact of the characterisation technique considered for the determination of the Li+ solid state diffusion coefficient in uncycled as in cycled Nickel Manganese Cobalt oxide (NMC) electrodes. As major characterisation techniques, Cyclic Voltammetry (CV), Galvanostatic Intermittent Titration Technique (GITT) and Electrochemical Impedance Spectroscopy (EIS) were systematically investigated. Li+ diffusion coefficients during the lithiation process of the uncycled and cycled electrodes determined by CV at 3.71 V are shown to be equal to 3.48×10−10 cm2 ·s−1 and 1.56×10−10 cm2 ·s−1 , respectively. The dependency of the Li+ diffusion with the lithium content in the electrodes is further studied in this paper with GITT and EIS. Diffusion coefficients calculated by GITT and EIS characterisations are shown to be in the range between 1.76×10−15 cm2 ·s−1 and 4.06×10−12 cm2 ·s−1 , while demonstrating the same decreasing trend with the lithiation process of the electrodes. For both electrode types, diffusion coefficients calculated by CV show greater values compared to those determined by GITT and EIS. With ageing, CV and EIS techniques lead to diffusion coefficients in the electrodes at 3.71 V that are decreasing, in contrast to GITT for which results indicate increasing diffusion coefficient. After long-term cycling, ratios of the diffusion coefficients determined by GITT compared to CV become more significant with an increase about 1 order of magnitude, while no significant variation is seen between the diffusion coefficients calculated from EIS in comparison to CV. [ABSTRACT FROM AUTHOR]

Freight transport affects urban welfare primarily through congestion and emissions. An urban consolidation centre is regarded as a solution that can reduce those negative impacts of freight vehicles on the city. However, previous experience has demonstrated the challenge that these centres face in being self-sustaining. Given their positive effects on the city, authorities have supported these logistics schemes with different types of accompanying measures. In order to bring to practitioners an overview of these measures, the paper presents the results of a literature review where the different measures are classified according to their financial support, their regulatory support and their indirect regulatory support. [ABSTRACT FROM AUTHOR]

The growing need for accurate estimation of by fitting battery's thermal and electrical performances at different operating conditions is crucial in its applications especially in electrified vehicles. This paper presents an effective method for developing a thermal and electrical modeling methodology for calculation thermal behavior of a lithium-ion cell and the voltage response under a current solicitation. The model was elaborated on three pouch cells with different battery chemistries for use in electrical vehicles/hybrid electrical vehicles, namely lithium iron phosphate, lithium nickel manganese cobalt oxide, and lithium titanium oxide (LTO). The model, implemented in a MATLAB/Simulink interface, uses an equivalent circuit and heat-generation equations coupled a thermal model. The three cell chemistries have been investigated using test procedures and thermal images at room temperature. The results of this study show that a temperature distribution to be fairly uniform after a complete discharge for the three chemistries with the lowest temperature gradient found for the LTO-based cell. Finally, comparison between simulation results and measured data under dynamic profiles shows a good correspondence with the measurements of the validation tests with errors lying between ±4% and 2 °C for the electrical and thermal model, respectively. [ABSTRACT FROM AUTHOR]

The negative impact of the automotive industry on climate change can be tackled by changing from fossil driven vehicles towards battery electric vehicles with no tailpipe emissions. However their adoption mainly depends on the willingness to pay for the extra cost of the traction battery. The goal of this paper is to predict the cost of a battery pack in 2030 when considering two aspects: firstly a decade of research will ensure an improvement in material sciences altering a battery's chemical composition. Secondly by considering the price erosion due to the production cost optimization, by maturing of the market and by evolving towards to a mass-manufacturing situation. The cost of a lithium Nickel Manganese Cobalt Oxide (NMC) battery (Cathode: NMC 6:2:2 ; Anode: graphite) as well as silicon based lithium-ion battery (Cathode: NMC 6:2:2 ; Anode: silicon alloy), expected to be on the market in 10 years, will be predicted to tackle the first aspect. The second aspect will be considered by combining process-based cost calculations with learning curves, which takes the increasing battery market into account. The 100 dollar/kWh sales barrier will be reached respectively between 2020-2025 for silicon based lithium-ion batteries and 2025-2030 for NMC batteries, which will give a boost to global electric vehicle adoption. [ABSTRACT FROM AUTHOR]

Limited driving range remains one of the barriers for widespread adoption of electric vehicles (EVs). To address the problem of range anxiety, this paper presents an energy consumption prediction method for EVs, designed for energy-efficient routing. This data-driven methodology combines real-world measured driving data with geographical and weather data to predict the consumption over any given road in a road network. The driving data are linked to the road network using geographic information system software that allows to separate trips into segments with similar road characteristics. The energy consumption over road segments is estimated using a multiple linear regression (MLR) model that links the energy consumption with microscopic driving parameters (such as speed and acceleration) and external parameters (such as temperature). A neural network (NN) is used to predict the unknown microscopic driving parameters over a segment prior to departure, given the road segment characteristics and weather conditions. The complete proposed model predicts the energy consumption with a mean absolute error (MAE) of 12–14% of the average trip consumption, of which 7–9% is caused by the energy consumption estimation of the MLR model. This method allows for prediction of energy consumption over any route in the road network prior to departure, and enables cost-optimization algorithms to calculate energy efficient routes. The data-driven approach has the advantage that the model can easily be updated over time with changing conditions. [ABSTRACT FROM AUTHOR]

Recently, second-life battery systems have received a growing interest as one of the most promising alternatives for decreasing the overall cost of the battery storage systems in stationary applications. The high-cost of batteries represents a prominent barrier for their use in traction and stationary applications. To make second-life batteries economically viable for stationary applications, an effective power-electronics converter should be selected as well. This converter should be supported by an energy management strategy (EMS), which is needed for controlling the power flow among the second-life battery modules based on their available capacity and performance. This article presents the design, analysis and implementation of a generic energy management strategy (GEMS). The proposed GEMS aims to control and distribute the load demand between battery storage systems under different load conditions and disturbances. This manuscript provides the experimental verification of the proposed management strategy. The results have demonstrated that the GEMS can robustly handle any level of performance inequality among the used-battery modules with the aim to integrate different levels (i.e., size, capacity, and chemistry type) of the second-life battery modules at the same time and in the same application. [ABSTRACT FROM AUTHOR]

In this paper, the environmental and economic impacts of the life cycle of an advanced lithium based energy storage system (ESS) for a battery electric vehicle are assessed. The methodology followed to perform the study is a Multiregional Input-Output (MRIO) analysis, with a world IO table that combines detailed information on national production activities and international trade data for 40 countries and a region called Rest of the World. The life cycle stages considered in the study are manufacturing, use and recycling. The functional unit is one ESS with a 150,000 km lifetime. The results of the MRIO analysis show the stimulation that the life cycle of the EES has in the economy, in terms of production of goods and services. The manufacturing is the life cycle stage with the highest environmental load for all the impact categories assessed. The geographical resolution of the results show the relevance that some countries may have in the environmental performance of the assessed product even if they are not directly involved in any of the stages of the life cycle, proving the significance of the indirect effects. [ABSTRACT FROM AUTHOR]

The combustion of fossil fuels in the transport sector leads to an aggravation of the air quality along city roads and highways. Urban air quality is a serious problem nowadays as the number of vehicles increases on a yearly basis. With stricter Euro emission regulations, vehicle manufacturers are not meeting the imposed limits and are also disregarding the non-exhaust emissions. This paper highlights the relevance of non-exhaust emissions of passenger vehicles, both conventional (diesel and petrol) or electric vehicles (EV), on air quality levels in an urban environment in Belgium. An environmental life cycle assessment was carried out based on a real-world emission model for passenger cars and fuel refinery data. A cut-off was applied to the models to highlight what emissions, both from the refinery to the exhaust and electricity production for EV, do actually occur within Belgium's borders. Results show that not much progress has been made from Euro 4 to 6 for conventional vehicles. Electric vehicles pose the best alternative solution as a more environmentally friendly means of transportation. The analysis results target policy makers with the intention that regulations and policies would be developed in the future and target the characterization of non-exhaust emissions from vehicles. These results indicate that EVs offer a valid solution for addressing the urban air quality issue and that non-exhaust emissions should be addressed in future regulatory steps as they dominate the impact spectrum. [ABSTRACT FROM AUTHOR]