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In electric vehicles (EVs), battery thermal management system (BTMS) plays an essential role in keeping the battery working within the optimal operating temperature range and preventing thermal runaway. Many cooling mediums have been conducted into BTMS to transfer, absorb, or dissipate the heat generated from the batteries. Thermal conductivity, heat transfer coefficient, cooling performance, cost, poison, environment, system size, and equipment needed are critical factors in choosing the ideal heat transfer coolant for the BTMS. This review paper concentrates on the novel and echo-friendly heat transfer coolant mediums investigated for BTMS and has been rarely documented in the literature. In the scope of this review, traditional BTMS coolant mediums including air, water, phase change material (PCM), and hybrid coolants are considered, and their optimization techniques have been discussed. Additionally, a comprehensive review is provided on novel techniques and novel materials that have the possibility of enhancing the thermal performance of the battery pack on the one hand, as well as the potential of integration into BTMS with higher safety and less (weight, volume, cost, toxicity, and power consumption) compared to the classical heat transfer coolant mediums on the other hand. Finally, evaporative, mist, spray, and nanofluid techniques are found as promising cooling techniques. In terms of environmental, availability, and non-toxicity aspect, jute has the highest possibility of being integrated into BTMS. This study will give the opportunity to see the latest research investigating novel cooling mediums, which will lead to further improvement for BTMS.

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.

The World Electric Vehicle Journal was started in 2007 with the aim of providing a platform for scientific communications within the field of electric vehicles [...]

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 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.

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 °C, 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.

Lithium-ion capacitors (LiCs) are commonly used as power sources for electric vehicles (EVs) due to the combined advantages of electric double-layer capacitors (EDLCs) and lithium-ion batteries (LiBs) comprising high energy density, high power density, and long lifetime. However, the performance of the LiCs is susceptible to temperature. Therefore, a robust thermal management system (TMS) is crucial for EVs to operate efficiently and safely. In this work, holistic active and passive TMSs are designed to control the maximum temperature of the cell. In this regard, an air-cooled TMS (ACTMS) and a compact liquid-cooled TMS (LCTMS) are among the active cooling systems, where pure paraffin phase change material (PCM), PCM with added aluminum mesh grid foil (PCM-Al), PCM with an added heat sink (PCM-HS), and heat pipe cooling system (HPCS) are the investigated passive TMSs. Moreover, the experimental results are verified against numerical analysis using a computational fluid dynamics (CFD) software, COMSOL Multiphysics. The most efficient active and passive cooling systems are then selected in the CFD simulations to make a robust hybrid TMS for a module of LiC cells. The results exhibit that the LCTMS has the best performance where the maximum temperature of the cell is controlled at 32.5 °C, which shows the reduction of 41.2% compared to the natural convection (NC) case study. The PCM-HS has the best performance among the passive cooling systems, decreasing 38.3% compared to the NC. Moreover, the hybrid TMS decreases the maximum temperature by 50.1% while uniformizing the temperature along with the module by keeping the temperature difference below 4.5 °C between the coldest and hottest points on the cell.

Nowadays, the use of electric vehicles (EVs) equipped with lithium-ion (Li-ion) batteries, has been growing every day. Li-ion batteries' performance, effectiveness, and safety importantly depend on thermal management systems (TMSs). In this paper, a novel and advanced hybrid TMS for cooling the battery module, using phase change material (PCM) and liquid cooling, has been experimentally studied. Passive PCM heat buffer plate and liquid cooling plates are connected from down and lateral sides, respectively. Cooling with natural convection could not preserve the module temperature in the desired operational temperature at fast charging. The average module temperature for the charging and discharging process reaches 41.4 °C and 43 °C respectively. The results display the module temperature equipped with a PCM heat buffer plate at the end of the charging and discharging process reaches 35.8 °C and 36.2 °C which experience a 13.3% and 15.8% temperature reduction, respectively. Using the hybrid cooling system, the module temperature at the end of the charging and discharging process reaches 31.2 °C and 31.8 °C which experience a 24.6% and 26% temperature reduction compared with natural convection. Moreover, using the hybrid cooling system the temperature uniformity has been improved by 56% and 34.8% for the charging and discharging process, respectively.

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%.

Recently, the phase change material (PCM) battery thermal management system (BTMS) attracted attention. However, enhancement and optimization for the BTMS are required due to volumetric system design and low thermal conductivity. This study provides a novel design optimization to improve the environmental aspect of the cooling system and reduce its weight. Jute fibers as a planet-system, available, cheap, and weightless material is combined with the PCM battery thermal management based. The thermal behavior of large lithium-ion batteries (LIB) under different load protocols including fast discharge, periodic load, and real drive cycle are investigated. The results with the periodic load profile confirm that the maximum temperature for the cooling strategies of no-cooling, PCM cooling and PCM with jute reaches 39.22 °C, 38.22 °C and 35.09 °C, respectively. Moreover, applying aggressive high constant discharge current leads to further reduction in maximum temperature, where the maximum temperature reaches 47.27 °C, 41.06 °C, and 36.29 °C with the no-cooling, PCM cooling and PCM with jute cooling strategies, respectively. This article represents the first attempt to use a combination of jute and PCM in order to maximize temperature efficiency enhanced. Thus, this research contributes to further design optimization in battery thermal management system simplicity, environmental friendliness, energy and weight saving.

Summary: Formation of a decent solid-electrolyte interphase (SEI) is recognized as an approach to improve the performance of lithium-ion batteries. SEI is a passivation layer generated on the anode during the initial cycles. Characteristics of the graphite SEI depend on the operational parameters, state of the anode, and the content of the electrolyte. Introducing reduction-type additives to the carbonate electrolytes has been one of the most practiced methods to generate an effective SEI on carbonous anodes. To track the role of additives in SEI evolution, first, we have presented a general review on what is currently understood about the SEI formation processes and the impacting parameters. In the second step, the most reported methods to study and analyze the functionality of the SEI-forming additives are classified. As the third part, different reduction-type additives are categorized, and their performances are comparatively reviewed.

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.

Lithium-ion capacitor (LiC) technology is an energy storage system (ESS) that combines the working mechanism of electric double-layer capacitors (EDLC) and lithium-ion batteries (LiB). When LiC is supposed to work under high power applications, the inevitable heat loss threatens the cell's performance and lifetime. Therefore, a proper thermal management system (TMS) can remove the generated heat of the LiC during high cycling conditions. In this paper, a hybrid TMS (HTMS) using phase change materials (PCM) and six flat heat pipes is proposed to maintain the temperature profile below 40 °C under a high current rate of 150 A for 1400 s profile without any pause. Two K-type thermocouples (T1 & T2) are responsible for monitoring the experiments' temperature evolution in the experiments. Numerical analysis is also performed and verified with experimental results to analyze the temperature profile numerically. The experimental and numerical simulation comprises three case studies, including the cell's temperature under natural convection, temperature distribution when using the heat pipe TMS, and temperature distribution when using HTMS. The results reveal that the HTMS is an exceptionally robust cooling system since it reduces the T1 temperature by 35% compared to the natural convection case study, while the heat pipe TMS can reduce the T1 temperature by 15% compared to the same case study.

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.

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.

This paper presents the concept of a passive thermal management system (TMS), including natural convection, heat pipe, and phase change material (PCM) for electric vehicles. Experimental and numerical tests are described to predict the thermal behavior of a lithium-titanate (LTO) battery cell in a high current discharging process. Details of various thermal management techniques are discussed and compared with each other. The mathematical models are solved by COMSOL Multiphysics®, the commercial computational fluid dynamics (CFD) software. The simulation results are validated against experimental data with an acceptable error range. Results indicate that the maximum cell temperature for the cooling strategies of natural convection, heat pipe, and PCM assisted heat pipe reaches 56 °C, 46.3 °C, and 33.2 °C, respectively. It is found that the maximum cell temperature experienced a 17.3% and 40.7% reduction by heat pipe and PCM assisted heat pipe cooling system compared with natural convection.

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.

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.

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.

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.

Summary: Lithium-ion battery technologies have conquered the current energy storage market as the most preferred choice thanks to their development in a longer lifetime. However, choosing the most suitable battery aging modeling methodology based on investigated lifetime characterization is still a challenge. In this work, a comprehensive aging dataset of nickel-manganese-cobalt oxide (NMC) cell is used to develop and/or train different capacity fade models to compare output responses. The assessment is conducted for semi-empirical modeling (SeM) approach against a machine learning model and an artificial neural network model. Among all, the nonlinear autoregressive network (NARXnet) can predict the capacity degradation most precisely minimizing the computational effort as well. This research work signifies the importance of lifetime methodological choice and model performance in understanding the complex and nonlinear Li-ion battery aging behavior.

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.

Fast charging is an essential stakeholder concern for achieving a deeper penetration of Electric Vehicles (EVs), as optimizing the charging times of conventional vehicles is as yet a bottleneck to be solved. An important drawback of EV’s fast charging lies in the degradation suffered by the Li-ion Batteries (LIBs) at high charging currents. A deep understanding of the how these fast-charging activities affect the LIBs’ degradation is necessary in order to design appropriate fast charging stations and EV powertrains for different scenarios and contexts. In this regard, the present paper analyzes the effect of fast charging on Libs’ degradation under operation profiles from real driving cycles. Specifically, Battery Electric Buses (BEBs) driving profiles from three demos in European Cities (Gothenburg, Osnabrück and Barcelona) have been used in this analysis. In order to deduce the best practices for the design of the charging stations, different sizes for the chargers have been simulated, focusing on the analysis of the LIB degradation under each situation. Besides, for the design of the EV powertrain, different LIB sizes and LIB chemistries (Lithium Nickel Manganese Cobalt-NMC, Lithium Iron Phosphate-LFP, and Lithium Titanate Oxide-LTO) have also been proposed and compared in terms of LIB degradation. The results demonstrated that LTO batteries exhibited the lowest degradation, with capacity fade values under 1.5%/year in the nominal scenario (nominal charger and LIB sizes). As long as a full charging is ensured, reducing the fast charger size has been found to be a cost-effective measure, as the LTO degradation can be reduced at least to 1.21%/year. In addition, increasing the battery (BT) size has also been found to be a cost-effective approach for LTO batteries. In this case, it was found that for a 66% increase in capacity, the degradation can be reduced at least to 0.74%/year (more than 50% reduction). The obtained conclusions are seen as useful for the design of charging stations and EV’s BT systems that undergo fast charging.

Battery Electric Buses (BEBs) are considerably integrated into cities worldwide. These buses have a strict schedule; thus, they could be charged in a very short time with a power level up to 600 kW. The high-power systems and short charging times imply special grid operation conditions that should be taken into account. Therefore, it is necessary to consider the influence of their charging infrastructure on the distribution system operation, especially near the charging point. This work presents two Use Cases (UCs) from two demos (Germany and the Netherlands) to investigate the impact of the slow and fast-chargers’ integrations on the power grid and environment. Fast-chargers up to 350 kW based on pantograph technology and slow-chargers up to 50 kW based on Combined Charging System Type 2 (CCS2) are used on the BEB line route and in the depot, respectively. The charging of BEBs with these solutions is studied here to investigate their impact on the grid in terms of power quality. It was found that the voltage variations due to fast-chargers terminal remain much below the EN50160 standard limit values i.e., ±10%. The obtained maximum Total Harmonic Voltage Distortion (THDv) value is 2.7%, with an average value of 1.3%, which is below the limit value of 8%, as per the standard EN 50160. Similarly, the individual harmonic currents were measured. The maximum value of total harmonic current distortion (THDI) is around 25%, with an average value of 3% only. As the average value of THDI is quite low, the harmonic current pollution is not a big concern for the installation at this time.

Solid-state batteries (SSBs) are gaining attention as they promise to provide better safety and a higher energy density than conventional liquid electrolyte batteries. Solid polymer electrolytes (SPEs) are promising candidates due to their flexibility providing better interfacial contact between electrodes and the electrolyte. However, SPEs exhibit very low ionic conductivity at ambient temperatures, which prevents their practical use in batteries. Herein, a simple and effective technique of hot press rolling is demonstrated to improve ionic conductivity and, hence, the performance of polyvinylidene fluoride-co-hexafluoropropylene (PVDF-HFP)-based solid polymer electrolyte. Applying hot press rolling to the electrolyte membrane induced structural changes in the grain boundaries, which resulted in a reduction in the crystallinity of the material and, hence, an increase in the amorphous phase of the material, which eased the movement of the lithium ions within the material. This technique also improved the surface of the membrane, making it homogeneous and smoother, which resulted in better interfacial contact between the electrodes and electrolyte. Electrochemical tests were carried out on electrolyte membranes treated with and without hot press rolling to evaluate the effect of the treatment. The hot pressed electrolyte membrane showed significant improvements in its ionic conductivity and transference number. The cycling performance of the LFP/Li batteries using a hot press rolled electrolyte was also evaluated, which gave a specific discharge capacity of 134 mAh/g at 0.1 C. These results demonstrate that hot press rolling can have a significant effect on the electrochemical performance of solid polymer electrolytes.

Jute is a cheap, eco-friendly, widely available material well-known for its cooling properties. In electric vehicles (EVs), dissipating a huge amount of the heat generated from lithium-ion batteries with an efficient, light, and low-power consumption battery thermal management system (BTMS) is required. In our previous study, jute fibers were proposed and investigated as a novel medium to enhance the cooling efficiency of air-based battery thermal management systems. In this paper, as the first attempt, jute was integrated with a phase change material (PCM) passive cooling system, and the thermal performance of a 50 Ah prismatic battery was studied. Temperature evolution, uniformity, and cooling efficiency were investigated. A comparison between the thermal behavior of the air-based BTMS and PCM-assisted cooling system was performed. The results indicated that adding jute to the BTMS increased the cooling efficiency and especially decreased the temperature development. Furthermore, the temperature difference (ΔT) efficiency was enhanced by 60% when integrating jute with PCM, and temperature uniformity improved by 3% when integrating jute with air-based BTMS. This article compared the integration of jute with active cooling and passive cooling; thus, it shed light on the importance of jute as a novel, eco-friendly, lightweight, cheap, available, and nontoxic material added to two strategies of BTMS. The setup was physically made and experimentally studied for the purpose of BTMS optimization.

This work presents three demos, which include Electric Buses (EBs) from four various brands with lengths of 12 m and 18 m and an Electric Truck (E-truck) for refuse collection. The technical operation of these EVs were analyzed to implement further operational cost optimization on the demo vehicles. The Electric Vehicles (EVs) were tested against superfast-charging solutions based on Pantograph (Type A & Type B) on the route lines (and depots) and based on Combined Charging System Type 2 (CCS2, Combo2) from various brands to validate the interoperability among several vendors and support further EV integration with more affordable solutions. The optimization includes the calculation of the EBs’ consumption at various seasons and under various operating conditions in order to use optimum battery system design, heating system, optimum EB fleet operation and size and to find the charging solutions properly. The results showed that the EB consumption increases in some cases by 64.5% in wintertime due to heating systems, and the consumption in urban areas is more than that on the route lines outside cities. In the E-truck demo, where the electric heater was replaced with a heat-pump to optimize the energy consumption, it was found that the consumption of the heat-pump is about half of the electric heater under certain operating conditions. Under strict EB schedule, Pantograph charging solutions with power ratings of 300–600 kW have been adopted to charge the batteries of the EBs within 4–10 min. In order to minimize the cumulative costs of energy, (pantograph) charging infrastructure depreciation and battery degradation, as well as depot charging (at the bus operator’s depot), was adopted with a power level of 50–350 kW based on CCS2 and pantograph.

The Brushless Doubly Fed Reluctance Machine (BDFRM) is an emerging alternative for variable speed drive systems, providing a significant downsizing of the power electronics converter. This paper proposes a new view on the machine equations, allowing the reuse of the standard control system design for conventional synchronous and asynchronous machines: a cascade control system with an inner current control- and outer speed control loop. The assumptions and simplifications made on the machine model allow for a simple, model-based approach to set the controller gains in a brushless doubly fed machine drive system. The cascade control scheme is combined with an Unscented Kalman Filter as a state observer, capable of estimating the load torque and losses. The performance of the proposed control system design is checked in simulation and tested in real-time on a low power BDFRM prototype.

The solid–electrolyte interphase (SEI), the passivation layer formed on anode particles during the initial cycles, affects the performance of lithium-ion batteries (LIBs) in terms of capacity, power output, and cycle life. SEI features are dependent on the electrolyte content, as this complex layer originates from electrolyte decomposition products. Despite a variety of studies devoted to understanding SEI formation, the complexity of this process has caused uncertainty in its chemistry. In order to clarify the role of the substituted functional groups of the SEI-forming compounds in their efficiency and the features of the resulting interphase, the performance of six different carbonyl-based molecules has been investigated by computational modeling and electrochemical experiments with a comparative approach. The performance of the electrolytes and stability of the generated SEI are evaluated in both half-cell and full-cell configurations. Added to the room-temperature studies, the cyclability of the NMC/graphite cells is assessed at elevated temperatures as an intensified aging condition. The results show that structural adjustments within the SEI-forming molecule can ameliorate the cyclability of the electrolyte, leading to a higher capacity retention of the LIB cell, where cinnamoyl chloride is introduced as a novel and more sustainable SEI forming agent with the potential of improving the LIB capacity retention.

This work presents an active thermal management system (TMS) for building a safer module of lithium-ion capacitor (LiC) technology, in which 10 LiCs are connected in series. The proposed TMS is a forced air-cooled TMS (ACTMS) that uses four axial DC 12 V fans: two fans are responsible for blowing the air from the environment into the container while two other fans suck the air from the container to the environment. An experimental investigation is conducted to study the thermal behavior of the module, and numerical simulations are carried out to be validated against the experiments. The main aim of the model development is the optimization of the proposed design. Therefore, the ACTMS has been optimized by investigating the impact of inlet air velocity, inlet and outlet positions, module rotation by 90° towards the airflow direction, gap spacing between neighboring cells, and uneven gap spacing between neighboring cells. The 3D thermal model is accurate, so the validation error between the simulation and experimental results is less than 1%. It is proven that the ACTMS is an excellent solution to keep the temperature of the LiC module in the desired range by air inlet velocity of 3 m/s when all the fans are blowing the air from both sides, the outlet is designed on top of the module, the module is rotated, and uneven gap space between neighboring cells is set to 2 mm for the first distance between the cells (d1) and 3 mm for the second distance (d2).

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.

This paper presents the concept of a hybrid thermal management system (TMS) including natural convection, heat pipe, and air cooling assisted heat pipe (ACAH) for electric vehicles. Experimental and numerical tests are described to predict the thermal behavior of a lithium titanate oxide (LTO) battery cell in a fast discharging process (8C rate). Specifications of different cooling techniques are deliberated and compared. The mathematical models are solved by COMSOL Multiphysics® (Stockholm, Sweden), the commercial computational fluid dynamics (CFD) software. The simulation results are validated against experimental data with an acceptable error range. The results specify that the maximum cell temperatures for the cooling systems of natural convection, heat pipe, and ACAH reach 56, 46.3, and 38.3 °C, respectively. We found that the maximum cell temperature experiences a 17.3% and 31% reduction with the heat pipe and ACAH, respectively, compared with natural convection.

The risk of oscillation of grid-connected wind turbine generators (WTGs) is well known, making it all the more important to understand the characteristics of different WTGs and analyze their performance so that the problems’ causes are identified and resolved. While many studies have evaluated the performance of grid-connected WTGs, most lack clarity and precision in the modeling and simulation techniques used. Moreover, most of the literature focuses on a single mode of operation of WTGs to analyze their performances. Therefore, this paper updates the literature by considering the different operating conditions for WTGs. Using MATLAB/SIMULINK it expands the evaluation to the full range of vulnerabilities of WTGs: from the wind turbine to grid connection. A network representing grid-connected squirrel-cage induction generator (SCIG) and doubly-fed induction generator (DFIG) wind turbines are selected for simulation. The performances of SCIG and DFIG wind turbines are evaluated in terms of their energy generation capacity during constant rated wind speed, variable wind speed, and ability of fault-ride through during dynamic system transient operating conditions. The simulation results show the performance of DFIG is better than SCIG in terms of its energy generation capacity during variable wind speed conditions and active and reactive power control capability during steady-state and transient operating conditions. As a result, DFIG wind turbine is more suitable for large-scale wind power plants connected to weak utility grid applications than SCIG.

The global electric vehicle (EV) is expanding enormously, foreseeing a 17.4% increase in compound annual growth rate (CAGR) by the end of 2027. The lithium-ion battery is considered as the most widely used battery technology in EV. The accurate and reliable diagnostic and prognostic of battery state guarantees the safe operation of EV and is crucial for durable electric vehicles. Research focusing on lithium-ion battery life degradation has grown more important in recent years. In this study, a model built for state of health (SoH) estimation for the LTO anode-based lithium-ion battery is presented. First, electrochemical impedance spectroscopy (EIS) is used to study the deterioration in battery performance, measurements such as charge transfer resistance and ohmic resistance are analyzed for different operational conditions and selected as key characteristic parameters for the model. Then, the model based on a backpropagation neural network (BPNN) along with the characteristic parameters is trained and validated with a real-life driving profile. The model shows a relatively accurate estimation of SoH with a mean-squared-error (MSE) of 0.002.

Thermal management is the most vital element of electric vehicles (EV) to control the maximum temperature of module/pack for safety reasons. This paper presents a novel passive thermal management system (TMS) composed of a heat sink (HS) and phase change materials (PCM) for lithium-ion capacitor (LiC) technology under the premise that the cell is cycled with a continuous 150 A fast charge/discharge current rate. The experiments are validated against numerical analysis through a computational fluid dynamics (CFD) model. For this purpose, a comprehensive electro-thermal model based on an equivalent circuit model (ECM) is designed. The designed electro-thermal model combines the ECM model with the thermal model since the performance of the LiC cell highly depends on the temperature. Then, the robustness of the model is evaluated using a precise second-order ECM. The extracted parameters of the electro-thermal model are verified by the experimental results in which the voltage and temperature errors are less than ±5% and ±4%, respectively. Finally, the thermal performance of the HS-assisted PCM TMS is studied under the fast charge/discharge current rate. The 3D CFD results exhibit that the temperature of the LiC when using the PCM-HS as the cooling system was reduced by 38.3% (34.1 °C) compared to the natural convection case study (55.3 °C).

Lithium-ion (Li-ion) batteries have emerged as a promising energy source for electric vehicle (EV) applications owing to the solution offered by their high power, high specific energy, no memory effect, and their excellent durability. However, they generate a large amount of heat, particularly during the fast discharge process. Therefore, a suitable thermal management system (TMS) is necessary to guarantee their performance, efficiency, capacity, safety, and lifetime. This study investigates the thermal performance of different passive cooling systems for the LTO Li-ion battery cell/module with the application of natural convection, aluminum (Al) mesh, copper (Cu) mesh, phase change material (PCM), and PCM-graphite. Experimental results show the average temperature of the cell, due to natural convection, Al mesh, Cu mesh, PCM, and PCM-graphite compared with the lack of natural convection decrease by 6.4%, 7.4%, 8.8%, 30%, and 39.3%, respectively. In addition, some numerical simulations and investigations are solved by COMSOL Multiphysics®, for the battery module consisting of 30 cells, which is cooled by PCM and PCM-graphite. The maximum temperature of the battery module compared with the natural convection case study is reduced by 15.1% and 17.3%, respectively. Moreover, increasing the cell spacing in the battery module has a direct effect on temperature reduction.

In electrified vehicle applications, understanding the battery characteristics is of great importance as it is the state-of-art principal energy source. The key battery parameters can be identified by one of the robust and nondestructive characterization techniques, such as electrochemical impedance spectroscopy (EIS). However, relaxing the battery cell before performing the EIS method is crucial for the characterization results to be standardized. In this study, the three most common and commercially available lithium-ion technologies (NMC/graphite, LFP/graphite, NCA/LTO) are investigated at 15–45 °C temperature, in the range of 20–80% state of charge (SoC) and in fresh and aged state of health (SoH) conditions. The analysis shows that the duration of the relaxation time before impedance measurement has an impact on the battery’s nonlinear behavior. A rest time of 2 h can be proposed, irrespective of battery health condition, considering neutral technology-based impedance measurement. An impedance growth in ohmic and charge transfer characteristics was found, due to aging, and the effect of rest periods was also analyzed from an electrochemical standpoint. This experimental data was fitted to develop an empirical model, which can predict the nonlinear dynamics of lithium technologies with a 4–8% relative error for longer rest time.

A lithium-ion capacitor (LiC) is one of the most promising technologies for grid applications, which combines the energy storage mechanism of an electric double-layer capacitor (EDLC) and a lithium-ion battery (LiB). This article presents an optimal thermal management system (TMS) to extend the end of life (EoL) of LiC technology considering different active and passive cooling methods. The impact of different operating conditions and stress factors such as high temperature on the LiC capacity degradation is investigated. Later, optimal passive TMS employing a heat pipe cooling system (HPCS) is developed to control the LiC cell temperature. Finally, the effect of the proposed TMS on the lifetime extension of the LiC is explained. Moreover, this trend is compared to the active cooling system using liquid-cooled TMS (LCTMS). The results demonstrate that the LiC cell temperature can be controlled by employing a proper TMS during the cycle aging test under 150 A current rate. The cell’s top surface temperature is reduced by 11.7% using the HPCS. Moreover, by controlling the temperature of the cell at around 32.5 and 48.8 °C, the lifetime of the LiC would be extended by 51.7% and 16.5%, respectively, compared to the cycling of the LiC under natural convection (NC). In addition, the capacity degradation for the NC, HPCS, and LCTMS case studies are 90.4%, 92.5%, and 94.2%, respectively.

The brushless doubly fed reluctance machine (BDFRM) is receiving an increased amount of attention from the research community thanks to its potential as an alternative drive for variable speed applications, both as motor and generator. Currently, the sizing of the BDFRM in the literature is based on the model of an ideal axially laminated rotor (ALR) and discrepancies are hidden in compensation factors which are in turn tuned with a finite element analysis (FEA). This paper proposes an analytical framework to accurately model the air gap field modulation, and by extension the torque density, of the BDFRM with ducted segmental rotor (DSR) and salient pole rotor (SPR). The results are verified with FEA and validated on a BDFRM prototype.

Today, there are many recent developments that focus on improving the electric vehicles and their components, particularly regarding advances in batteries, energy management systems, autonomous features and charging infrastructure. This plays an important role in developing next electric vehicle generations, and encourages more efficient and sustainable eco-system. This paper not only provides insights in the latest knowledge and developments of electric vehicles (EVs), but also the new promising and novel EV technologies based on scientific facts and figures—which could be from a technological point of view feasible by 2030. In this paper, potential design and modelling tools, such as digital twin with connected Internet-of-Things (IoT), are addressed. Furthermore, the potential technological challenges and research gaps in all EV aspects from hard-core battery material sciences, power electronics and powertrain engineering up to environmental assessments and market considerations are addressed. The paper is based on the knowledge of the 140+ FTE counting multidisciplinary research centre MOBI-VUB, that has a 40-year track record in the field of electric vehicles and e-mobility.

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.

Improved management and impermeability of refrigerants is a leading solution to reverse global warming. Therefore, crank-driven reciprocating refrigerator compressors are gradually replaced by more efficient, oil-free and hermetic linear compressors. However, the design and operation of an electromagnetic actuator, fitted on the compression requirements of a reciprocating linear compressor, received limited attention. Current research mainly focuses on the optimisation of short stroke linear compressors, while long stroke compressors benefit from higher isentropic and volumetric efficiencies. Moreover, designing such a system focuses mainly on the trade-off between number of copper windings and the current required, due to the large computational cost of performing a full geometric design optimisation based on a Finite Element Method. Therefore, in this paper, a computationally-efficient, multi-objective design optimisation for six geometric design parameters has been applied on a solenoid driven linear compressor with a stroke of 44.2 mm. The proposed multi-fidelity optimisation approach takes advantage of established models for actuator optimisation in mechatronic applications, combined with analytical equations established for a solenoid actuator to increase the overall computational efficiency. This paper consists of the multi-fidelity optimisation algorithm, the analytic model and Finite Element Method of a solenoid and the optimised designs obtained for optimised power and copper volume, which dominates the actuator cost. The optimisation results illustrate a trade-off between minimising the peak power and minimising the volume of copper windings. Considering this trade-off, an intermediate design is highlighted, which requires 33.3% less power, at the expense of an increased copper volume by 5.3% as opposed to the design achieving the minimum copper volume. Despite that the effect of the number of windings on the input current remains a dominant design characteristic, adapting the geometric parameters reduces the actuator power requirements significantly as well. Finally, the multi-fidelity optimisation algorithm achieves a 74% reduction in computational cost as opposed to an entire Finite Element Method optimisation. Future work focuses on a similar optimisation approach for a permanent magnet linear actuator.

The success of electric vehicles (EVs) depends principally on their energy storage system. Lithium-ion batteries currently feature the ideal properties to fulfil the wide range of prerequisites specific to electric vehicles. Meanwhile, the precise estimation of batteries’ state of health (SoH) should be available to provide the optimal performance of EVs. This study attempts to propose a precise, real-time method to estimate lithium-ion state of health when it operates in a realistic driving condition in the presence of dynamic stress factors. To this end, a real-life driving profile was simulated based on highly dynamic worldwide harmonized light vehicle test cycle load profiles. Afterward, various features will be extracted from voltage data and they will be scored based on prognostic metrics to select diagnostic features which can conveniently identify battery degradation. Lastly, an ensemble learning model was developed to capture the correlation of diagnostic features and battery’s state of health (SoH). The result illustrates that the proposed method has the potential to estimate the SoH of battery cells aged under a distinct depth of discharge and current profile with a maximum error of 1%. This confirms the robustness of the developed approach. The proposed method has the capability of implementing in battery management systems due to many reasons; firstly, it is tested and validated based on the data which are equal to the real-life driving operation of an electric vehicle. Secondly, it has high accuracy and precision, and a low computational cost. Finally, it can estimate the SoH of battery cells with different aging patterns.

This paper investigates the thermal behaviour of a large lithium iron phosphate (LFP) battery cell based on its electrochemical-thermal modelling for the predictions of its temperature evolution and distribution during both charge and discharge processes. The electrochemical-thermal modelling of the cell is performed for two cell geometry approaches: homogeneous (the internal region is considered as a single region) and discrete (the internal region is split into smaller regions for each layer inside the cell). The experimental measurements and the predictions of the cell surface temperature achieved with the simulations for both approaches are in good agreement with 1.5 °C maximum root mean square error. From the results, the maximum cell surface temperature and temperature gradient between the internal and the surface regions are around 31.3 °C and 1.6 °C. The temperature gradient in the radial direction is observed to be greater about 1.1 °C compared to the longitudinal direction, which is caused by the lower thermal conductivity of the cell in the radial compared to the longitudinal direction. During its discharge, the reversible, the ohmic and the reaction heat generations inside the cell reach up to 2 W, 7 W and 17 W respectively. From the comparison of the two modelling approaches, this paper establishes that the homogeneous modelling of the cell internal region is suitable for the study of a single cylindrical cell and is appropriate for the two-dimensional thermal behaviour investigation of a battery module made of multiple cells.

Electric vehicle (EV) energy consumption is variable and dependent on a number of external factors such as road topology, traffic, driving style, ambient temperature, etc. The goal of this paper is to detect and quantify correlations between the kinematic parameters of the vehicle and its energy consumption. Real-world data of EV energy consumption are used to construct the energy consumption calculation models. Based on the vehicle dynamics equation as underlying physical model, multiple linear regression is used to construct three models. Each model uses a different level of aggregation of the input parameters, allowing predictions using different types of available input parameters. One model uses aggregated values of the kinematic parameters of trips. This model allows prediction with basic, easily available input parameters such as travel distance, travel time, and temperature. The second model extends this by including detailed acceleration data. The third model uses the raw data of the kinematic parameters as input parameters to predict the energy consumption. Using detailed values of kinematic parameters for the prediction in theory increases the link between the statistical model and its underlying physical principles, but requires these parameters to be available as input in order to make predictions. The first two models show similar results. The third model shows a worse fit than the first two, but has a similar accuracy. This model has great potential for future improvement.

The non-uniform surface temperature distribution of a battery cell results from complex reactions inside the cell and makes efficient thermal management a challenging task. This experimental work attempts to determine the evolution of surface temperature distribution of three pouch type commercial cells: Nickel Manganese Cobalt oxide (NMC)-based 20 Ah cell, Lithium Iron Phosphate (LFP) 14 Ah, and Lithium Titanate Oxide (LTO) 5 Ah battery cell by using contact thermistor and infrared (IR) thermography. High current (up to 100 A) continuous charge/discharge and high current (80 A) micro pulse cycling profile were applied on the cells. It was found that thermistor based temperature profile varied cell to cell, especially the LTO cell. Among the investigated cells, the NMC cell shows highest temperature rise and the LTO cell the lowest rise. IR (Infrared) images revealed the spatial distribution of surface temperature, in particular the location of the hottest region varies depending not only on the geometrical and material properties of the cell, but also the type of loads applied on the cells. Finally, a modeling perspective of the cell temperature non-uniformity is also discussed.

Lithium-based batteries are considered as the most advanced batteries technology, which can be designed for high energy or high power storage systems. However, the battery cells are never fully identical due to the fabrication process, surrounding environment factors and differences between the cells tend to grow if no measures are taken. In order to have a high performance battery system, the battery cells should be continuously balanced for maintain the variation between the cells as small as possible. Without an appropriate balancing system, the individual cell voltages will differ over time and battery system capacity will decrease quickly. These issues will limit the electric range of the electric vehicle (EV) and some cells will undergo higher stress, whereby the cycle life of these cells will be shorter. Quite a lot of cell balancing/equalization topologies have been previously proposed. These balancing topologies can be categorized into passive and active balancing. Active topologies are categorized according to the active element used for storing the energy such as capacitor and/or inductive component as well as controlling switches or converters. This paper proposes an intelligent battery management system (BMS) including a battery pack charging and discharging control with a battery pack thermal management system. The BMS user input/output interfacing. The battery balancing system is based on battery pack modularization architecture. The proposed modularized balancing system has different equalization systems that operate inside and outside the modules. Innovative single switched capacitor (SSC) control strategy is proposed to balance between the battery cells in the module (inside module balancing, IMB). Novel utilization of isolated bidirectional DC/DC converter (IBC) is proposed to balance between the modules with the aid of the EV auxiliary battery (AB). Finally an experimental step-up has been implemented for the validation of the proposed balancing system.

How to compare the environmental performance of different vehicle technologies? Vehicles with lower tailpipe emissions are perceived as cleaner. However, does it make sense to look only to tailpipe emissions? Limiting the comparison only to these emissions denies the fact that there are emissions involved during the production of a fuel and this approach gives too much advantage to zero-tailpipe vehicles like battery electric vehicles (BEV) and fuel cell electric vehicle (FCEV). Would it be enough to combine fuel production and tailpipe emissions? Especially when comparing the environmental performance of alternative vehicle technologies, the emissions during production of the specific components and their appropriate end-of-life treatment processes should also be taken into account. Therefore, the complete life cycle of the vehicle should be included in order to avoid problem shifting from one life stage to another. In this article, a full life cycle assessment (LCA) of petrol, diesel, fuel cell electric (FCEV), compressed natural gas (CNG), liquefied petroleum gas (LPG), hybrid electric, battery electric (BEV), bio-diesel and bio-ethanol vehicles has been performed. The aim of the manuscript is to investigate the impact of the different vehicle technologies on the environment and to develop a range-based modeling system that enables a more robust interpretation of the LCA results for a group of vehicles. Results are shown for climate change, respiratory effects, acidification and mineral extraction damage of the different vehicle technologies. A broad range of results is obtained due to the variability within the car market. It is concluded that it is essential to take into account the influence of all the vehicle parameters on the LCA results.

A lithium-ion battery cell’s electrochemical performance can be obtained through a series of standardized experiments, and the optimal operation and monitoring is performed when a model of the Li-ions is generated and adopted. With discrete-time parameter identification processes, the electrical circuit models (ECM) of the cells are derived. Over their wide range, the dual-polarization (DP) ECM is proposed to characterize two prismatic cells with different anode electrodes. In most of the studies on battery modeling, attention is paid to the accuracy comparison of the various ECMs, usually for a certain Li-ion, whereas the parameter identification methods of the ECMs are rarely compared. Hence in this work, three different approaches are performed for a certain temperature throughout the whole SoC range of the cells for two different load profiles, suitable for light- and heavy-duty electromotive applications. Analytical equations, least-square-based methods, and heuristic algorithms used for model parameterization are compared in terms of voltage accuracy, robustness, and computational time. The influence of the ECMs’ parameter variation on the voltage root mean square error (RMSE) is assessed as well with impedance spectroscopy in terms of Ohmic, internal, and total resistance comparisons. Li-ion cells are thoroughly electrically characterized and the following conclusions are drawn: (1) All methods are suitable for the modeling, giving a good agreement with the experimental data with less than 3% max voltage relative error and 30 mV RMSE in most cases. (2) Particle swarm optimization (PSO) method is the best trade-off in terms of computational time, accuracy, and robustness. (3) Genetic algorithm (GA) lack of computational time compared to PSO and LS (4) The internal resistance behavior, investigated for the PSO, showed a positive correlation to the voltage error, depending on the chemistry and loading profile.

Climate change, urban air quality, and dependency on crude oil are important societal challenges. In the transportation sector especially, clean and energy-efficient technologies must be developed. Electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs) have gained a growing interest in the vehicle industry. Nowadays, the commercialization of EVs and PHEVs has been possible in different applications (i.e., light duty, medium duty, and heavy duty vehicles) thanks to the advances in energy-storage systems, power electronics converters (including DC/DC converters, DC/AC inverters, and battery charging systems), electric machines, and energy efficient power flow control strategies. This Special Issue is focused on the recent advances in electric vehicles and (plug-in) hybrid vehicles that address the new powertrain developments and go beyond the state-of-the-art (SOTA).