Hybrid Electric Vehicle Battery Research Articles

Online battery aging calculation in plug-in hybrid electric vehicle considering driving condition

ABSTRACT Online calculation of battery capacity loss and its instantaneous remaining life for electric and hybrid electric vehicles is a challenging subject, particularly for designing and optimizing vehicle energy management in real-life driving conditions. This paper presents an online method for estimating plug-in hybrid electric vehicle battery degradation, considering the driving conditions. For this purpose, a national parallel plug-in hybrid electric vehicle is employed, where its components have been sized based on some experimental map characteristics using a genetic algorithm. Also, a real-world driving cycle is developed based on the data gathered in the actual traffic conditions in Tehran. The vehicle’s energy management system is designed using an adaptive equivalent consumption minimization strategy. In addition, an empirical battery life model combined with an online cycle counting method is used for battery aging calculation. To evaluate the effectiveness of the proposed approach, the capacity fade of the vehicle battery over various driving cycles has been calculated. The comparison of the results confirms the efficacy of the proposed algorithm compared to the Rainflow counting algorithm. Applying the Rainflow to all driving cycles yields consistent outcomes. However, when employing the suggested method, capacity losses of approximately 2.4%, 1.9%, 2.1%, and 1.4% are detected in Tehran, FTP-75, WLTP, and NEDC, respectively, following one hundred driving cycles. This means the capacity loss increases by 35%, 50%, and 71% for FTP-75, WLTP, and Tehran, respectively, compared with the NEDC driving cycle.

Robotic Disassembly Platform for Disassembly of a Plug-In Hybrid Electric Vehicle Battery: A Case Study

Efficient processing of end-of-life lithium-ion batteries in electric vehicles is an important and pressing challenge in a circular economy. Regardless of whether the processing strategy is recycling, repurposing, or remanufacturing, the first processing step will usually involve disassembly. As battery disassembly is a dangerous task, efforts have been made to robotise it. In this paper, a robotic disassembly platform using four industrial robots is proposed to automate the non-destructive disassembly of a plug-in hybrid electric vehicle battery pack into modules. This work was conducted as a case study to demonstrate the concept of the autonomous disassembly of an electric vehicle battery pack. A two-step object localisation method based on visual information is used to overcome positional uncertainties from different sources and is validated by experiments. Also, the unscrewing system is highlighted, and its functions, such as handling untightened fasteners, loosening jammed screws, and changing the nutrunner adapters with square drives, are detailed. Furthermore, the time required for each operation is compared with that taken by human operators. Finally, the limitations of the platform are reported, and future research directions are suggested.

Use of Lithium-ion batteries in environmentally friendly vehicles

The concern for the environment and energy has led to a significant increase in research on electric vehicles (EVs) and hybrid electric vehicles (HEVs). The main challenges that need to be addressed to popularize these advanced vehicles are related to the battery. Since the early 1990s, we have been actively promoting research and development on batteries for environmentally friendly vehicle applications. Initially, the focus was on lithium-ion batteries as a potential solution to the critical battery issue mentioned earlier. Through extensive theoretical studies and numerous experimental demonstrations, we have successfully demonstrated that the potential of lithium-ion batteries exists and can be realized. This has opened up new possibilities and values that cannot be achieved with conventional batteries. This article aims to clarify the specific performance requirements of advanced batteries for EVs or HEVs, with a particular emphasis on the critical aspects of thermal design and construction for ensuring system stability. Furthermore, it highlights the significant improvements made in the power output of lithium-ion batteries for their application in HEVs.

Desain dan Simulasi Model Predictive Control pada Sistem Pembagian Daya untuk Kendaraan Listrik Hibrida Fuel Cell – Baterai

This research examines the performance of implementing Model Predictive Control (MPC) in the energy management system of fuel cell – battery hybrid electric vehicles. Three parameter variations were carried out on the MPC controller, namely variations in the horizon value, objective function, and weighting in the objective function. In testing variations in the horizon value of the designed MPC controller, it shows that the higher the horizon value used, the SOC and the final SOC are faster and closer to the optimal SOC determined by compensating for higher hydrogen consumption. Testing of objective function variations shows that the objective function implemented on the MPC controller influences the system response characteristics. It was found in objective function testing that optimal use of fuel cell power produces fuel cell power output with a working efficiency range of 57% - 60% when compared to other objective functions that work in the efficiency range of 49.2% - 57%. Finally, in testing variations in weighting values, it was found that the higher the weighting of an expression in the objective function, the more the optimizer will penalize the expression so that the solver will minimize the expression in the optimization process. Therefore, the MPC controller parameter values ​​need to be paid attention to so that the response characteristics are in accordance with the design. which are desired. It should be noted that this research does not use a speed prediction model so it is assumed that the speed is known without any uncertainty.

IoT-Enabled Deep Learning Algorithm for Estimation of State-of-Charge of Lithium-ion Batteries

Battery Management System (BMS) functions to monitor individual cell in a battery pack and its crucial task is to maintain stability throughout the battery pack. The BMS is responsible for maintaining the safety of the battery as well as not to harm the user or environment. The parameters that are to be monitored in a battery are Voltage, Current and Temperature. With the collected data, BMS carefully monitors the charging–discharging behavior of the battery particularly in the Lithium-ion (Li-ion) batteries in which charging and discharging behavior are completely different. This paper proposes a real-time IOT connected deep learning algorithm for estimation of State-of-Charge (SoC) of Li-ion batteries. This paper provides unique objectives and congruence between model-based conventional methods and state-of-the-art deep learning algorithm, specifically Feed Forward Neural Network (FNN) which is nonRecurrent. This paper also highlights the advantages of Internet-of-Things (IoT) connected deep learning algorithm for estimation of State-of-Charge of Li-ion batteries in Hybrid Electric Vehicles (HEVs) and Electric Vehicles (EVs). The major advantage of the proposed method is that the Artificial Intelligence (AI)-based techniques aim to bring the estimation error less than 2% at a low cost and time without the model of the battery, at par with conventional method of Extended Kalman Filter (EKF) which is the best ever practical estimation theory. Another advantage of the proposed method is that in an abnormal condition (i.e., Unsafe Temperature) the IF This Then That (IFTTT) IoT mobile application interfaced with BMS through ThingSpeak cloud, sends a notification alert to the battery expert or to the user prior to an emergency. Finally, the real-time data of the battery parameters are collected through ThingSpeak cloud platform for future research and analysis.

Tracing the Powerfade: Location and Quantification of the Fluoridic Solid Electrolyte Interphase on Graphite Anodes

Due to the conversion of the global energy supply from carbon emitting to renewable sources, there is the need for energy storage systems being efficient at high energy- and power densities. Many requirements are fulfilled best by Lithium-Ion Batteries (LIBs), so this technology is successfully part of battery-electric vehicles, cordless power tools and portable electronic devices. The ongoing improvement of anode- and cathode materials, together with adapted electrolytes [1] led to excellent advances in power- and energy densities. However, the increasing amount of total energy per cell underlines the importance of monitoring the cell’s health parameters (e.g. capacity fades, coulombic efficiencies, AC-and DC-impedances) in order to ensure safety and expand the lifetime [2, 3]. The Solid Electrolyte Interphase (SEI) represents a main source for cell impedances [4] and was in the case of LiPF6-based electrolytes found to be best analytically accessible by the quantitative determination of its unsolvable, fluoridic fractions (LiF) using ion-exchange chromatography [5].During this work, long term cycling tests (T = 25, 40, 60 °C, 2 C charging) of industrial 3 Ah-21700-cells with graphite anode (figure a), NMC 811 cathode and LiPF6-based electrolyte were performed (figure b). Subsequently, after defined cycling steps (10, 250, 500, 750 cycles) the cells were opened, post-mortem analysis (SEM, EDX, “broad ion beam”- (BIB) preparation) together with the drawing of anode/separators samples for the later fluoride determination took place. The samples were cleaned from LiPF6 (washing with DEC) and dried under glovebox conditions (Ar, < 0.5 ppm H2O). With the aim to quantify the SEI layer 1) in the anode bulk and 2) adhering to or inside the separator to trace reversible Li-plating, anode/separators samples were eluted separately with deionized water and the fluoride-concentration of the solutions was determined by ion-exchange chromatography (figure c - f). SEI fractions, like inorganic carbonates, alkyl carbonates, oxides, etc., were investigated using X-ray photoelectron spectroscopy (XPS) along a depth profile (figure g) using Ar-ion sputtering [6, 7].The results elaborated within this study point out a strong correlation in between the amount of fluoridic SEI and the DC-impedance rise during cycling, especially at elevated temperatures (40, 60 °C). At 25 °C different ageing mechanisms are obvious: In comparison lower fluoride concentrations together with substantial DC impedance gains suggest the temporary and superficial occurrence of reversible Li-plating, blocking important Li-ion paths via the electrolyte (e.g. anode surface pores, separator pores [8, 9]) by residual products of their SEI-films. Reference s [1] Eshetu, G. G.; Zhang, H.; Judez, X.; Adenusi, H.; Armand, M.; Passerini, S.; Figgemeier, E., Production of high-energy Li-ion batteries comprising silicon-containing anodes and insertion-type cathodes. Nat Commun 2021, 12, (1), 5459.[2] Sternad, M.; Cifrain, M.; Watzenig, D.; Brasseur, G.; Winter, M., Condition monitoring of Lithium-Ion Batteries for electric and hybrid electric vehicles. e & i Elektrotechnik und Informationstechnik 2009, 126, (5), 186-193.[3] Furtmair, M.; Wolters, A.; Kühnel, F.; Thannhuber, M.; Sötz, V.; Sternad, M., The Impact of Fast-Charging on Cell Ageing of Industrial High-Power Lithium-Ion Batteries. In IMLB 2022, Sydney, Australia, 2022.[4] Aurbach, D.; Markovsky, B.; Rodkin, A.; Cojocaru, M.; Levi, E.; Kim, H.-J., An analysis of rechargeable lithium-ion batteries after prolonged cycling. Electrochimica Acta 2002, 47, (12), 1899-1911.[5] Uitz, M.; Sternad, M.; Breuer, S.; Täubert, C.; Traußnig, T.; Hennige, V.; Hanzu, I.; Wilkening, M., Aging of Tesla's 18650 Lithium-Ion Cells: Correlating Solid-Electrolyte-Interphase Evolution with Fading in Capacity and Power. Journal of The Electrochemical Society 2017, 164, (14), A3503-A3510.[6] Shutthanandan, V.; Nandasiri, M.; Zheng, J.; Engelhard, M. H.; Xu, W.; Thevuthasan, S.; Murugesan, V., Applications of XPS in the characterization of Battery materials. Journal of Electron Spectroscopy and Related Phenomena 2019, 231, 2-10.[7] Zhu, Y.; Pande, V.; Li, L.; Wen, B.; Pan, M. S.; Wang, D.; Ma, Z. F.; Viswanathan, V.; Chiang, Y. M., Design principles for self-forming interfaces enabling stable lithium-metal anodes. Proc Natl Acad Sci U S A 2020, 117, (44), 27195-27203.[8] Lagadec, M. F.; Ebner, M.; Zahn, R.; Wood, V., Communication—Technique for Visualization and Quantification of Lithium-Ion Battery Separator Microstructure. Journal of The Electrochemical Society 2016, 163, (6), A992-A994.[9] Zier, M.; Scheiba, F.; Oswald, S.; Thomas, J.; Goers, D.; Scherer, T.; Klose, M.; Ehrenberg, H.; Eckert, J., Lithium dendrite and solid electrolyte interphase investigation using OsO4. Journal of Power Sources 2014, 266, 198-207. Acknowledgment The authors wish to thank H. Schröttner (Institute of Electron Microscopy and Nanoanalysis, Graz University of Technology) for preferential access to preparation and examination equipment. Figure 1

Research on SOC estimation method of hybrid electric vehicles battery based on the grey wolf optimized particle filter

In this paper, we propose a method called grey wolf optimized particle filter (GWO-PF) to estimate the state of charge (SOC) of the power battery in hybrid electric vehicles (HEVs). The GWO-PF method combines the particle distribution mechanism with grey wolf optimization to achieve accurate SOC estimation. To begin, we compare four different equivalent circuit models of power batteries and select the second-order RC model (RC2 model) as our research focus. Next, we identify the parameters of the RC2 model online using the recursive least square method with a forgetting factor. Finally, we utilize the identified parameters to implement the GWO-PF method for SOC estimation. We compare the performance of the GWO-PF method with two other commonly used methods: the unscented Kalman filter (UKF) and the particle filter (PF). The results demonstrate that the GWO-PF method achieves high precision in SOC estimation, with a controllable relative error within 3.5%.

Energy management optimization in smart railway stations with the ability to charge plug-in hybrid electric vehicles

The increasing development of traffic in the world has led to an increase in the consumption of electrical energy in electric vehicles (EVs). One of the most widely used electric vehicles is the electric train. Thus, energy optimization becomes more crucial in electrical railways to reduce consumption and total electricity bill as well as environmental issues. Consequently, electrical railway energy management must be technically and economically efficient and effective. This paper proposes an energy efficiency optimization framework for intelligent railway stations that provide plug-in electric vehicle charging parking capacity use, renewable energy sources (REs), and regenerative braking energy (RBE). The structure of the proposed convex programming (CP) model is used for fast and efficient optimization of decision variables, Equipment size, and the cost function of the station for reducing the power purchased from the Grid. Furthermore, by presenting meta-heuristic algorithms, the state variables of the energy storage system (ESS) and Plug-in hybrid electric vehicles (PHEVs) battery have been optimized. The results obtained from the proposed method prove that the operation cost of the station decreases by 61.4 %.

Degradation Evaluation of Lithium-Ion Batteries in Plug-In Hybrid Electric Vehicles: An Empirical Calibration

Battery life management is critical for plug-in hybrid electric vehicles (PHEVs) to prevent dangerous situations such as overcharging and over-discharging, which could cause thermal runaway. PHEVs have more complex operating conditions than EVs due to their dual energy sources. Therefore, the SOH estimation for PHEV vehicles needs to consider the specific operating characteristics of the PHEV and make calibrations accordingly. Firstly, we estimated the initial SOH by combining data-driven and empirical models. The data-driven method used was the incremental state of charge (SOC)-capacity method, and the empirical model was the Arrhenius model. This method can obtain the battery degradation trend and predict the SOH well in realistic applications. Then, according to the multiple characteristics of PHEV, we conducted a correlation analysis and selected the UF as the calibration factor because the UF has the highest correlation with SOH. Finally, we calibrated the parameters of the Arrhenius model using the UF in a fuzzy logic way, so that the calibrated fitting degradation trends could be closer to the true SOH. The proposed calibration method was verified by a PHEV dataset that included 11 vehicles. The experiment results show that the root mean square error (RMSE) of the SOH fitting after UF calibration can be decreased by 0.2–14% and that the coefficient of determination (R2) for the calibrated fitting trends can be improved by 0.5–32%. This provides more reliable guidance for the safe management and operation of PHEV batteries.

Fully soft-switched half-bridge DC–DC converter for hybrid electric vehicle battery charge applications

Fully soft-switched half-bridge DC–DC converter for hybrid electric vehicle battery charge applications

Lead single atoms anchored on reduced graphene oxide as multifunctional additive for lead–carbon battery

The application of lead–carbon batteries (LCBs) in hybrid electric vehicles and large-scale energy storage was limited by gradual sulfation and parasitic hydrogen evolution reaction (HER) of negative plates. Here, Pb single atoms anchored on reduced graphene oxide (PbSAs@rGO) were prepared through electrostatic adsorption and thermal reduction method, and confirmed by two aberration-corrected transmission electron microscopy (TEM and STEM) and synchrotron-based X-ray absorption fine structure spectroscopy (XAFS). By anchoring PbSAs on rGO surfaces where HER is prone to occur, the HER rate was greatly reduced. Moreover, PbSAs could serve as seeds to induce three-dimensional growth of Pb branches, which endows the NAM containing PbSAs@rGO additives with a specific surface areas (SSA) about 2.3 times than that of control one. The increased SSA can help to delay the sulfation of negative plate. Meanwhile, PbSAs can help enhance the affinity of rGO towards negative active material (NAM), which enable electrochemical reduction reaction proceeds at a higher rate. Thanks to the coordination of various functions from multifunctional additives, service life of LCBs under high-rate partial-state-of-charge (HRPSoC) have been extend from 3529 to 16,097 cycles, and from 4692 to 22,606 cycles, in 50% state of charge (SoC) and 75% SoC, respectively.

The Use of a Vortex Generator for the Efficient Cooling of Lithium-Ion Batteries in Hybrid Electric Vehicles

High heat flux dissipation from the Lithium-ion battery pack of hybrid electric vehicles is one of the major concerns in the automotive sector, since it directly affects the performance and it may also lead to permanent failure. Among various thermal management systems, forced air cooling is most favorable due to its light weight, compactness, lower cost, and design flexibility. In the present work, a battery thermal management system with the two types of vortex generator in the coolant passage has been used to enhance the performance. A numerical model has been developed in commercial code to investigate the performance of the delta winglet and circular protrusion type vortex generator. Apart from that, both types of vortex generator have been compared in terms of various parameters such as pressure drop, weight, and maximum temperature. From the results of the simulation, it has been observed that both vortex generators performed better at the attack angle of 30° in comparison to the attack angles of 45° and 60°. Furthermore, it has also been found that the cooling system with protrusion as the vortex generator has performed better in terms of thermos-hydraulic performance compared to the cooling system with the delta winglet vortex generator. In addition to that, the protruded system is lighter in weight compared to both the plain and delta winglet systems, and is recommended.

A statistical assessment of the state-of-health of LiFePO4 cells harvested from a hybrid-electric vehicle battery pack

Second-life battery applications can reduce the environmental impacts and life-cycle costs of electrochemical energy storage. The feasibility of reusing battery packs is strongly influenced by their modules' and cells' state-of-health. This paper analyzes the statistical distribution of state-of-health of cells in a retired BAE-Systems ESS-A123 hybrid-bus battery pack, consisting of 16 modules with LiFePO4 26,650 cells in a 12s8p configuration. We systematically sampled cells from the pack and collected several categories of state-of-health data. We provide three quantitative descriptions of the statistical distributions in SOH that can be used to model variation and uncertainties in 2nd-life feedstocks, including a linear regression between direct-current internal resistance (DCIR) and percent initial capacity (PIC) and fitted distributions for each variable. We found that SOH variation and mismatch within the modules is high in used HEV packs, making it infeasible to reuse most of them as-is. We illustrate with two feasible acceptance criteria adopted that the number of cells and modules that are accepted for second-life applications (90–99 % and 75–94 %, respectively) can vary drastically based on the criteria, illustrating the sensitivity of a 2nd-life business model to the definition of ‘faulty’ cells and modules. Finally, we found a key frequency (89 Hz) at which the correlations between AC impedance and constant-current cycling parameters are particularly strong, suggesting that cheap, fast EIS tests can potentially substitute longer diagnostic tests, thus lowering the cost of refurbishment.

A novel thermal management system for battery packs in hybrid electrical vehicles utilising waste heat recovery

• Waste heat from HEVs is applied to the ejector battery thermal management system. • Accurate prediction of complex flow behaviors in ejector by the condensation model. • Ejector's COP and ER shows inverse trend with increasing inlet superheating. • Ejector's performance reaches maximum with an increase of 11 K inlet superheating. Thermal management system generally ensures the safe operating conditions and heat resilience of battery packs in hybrid electric vehicles (HEVs). The current study raised a novel approach to reduce fire risks related to HEVs through a novel battery thermal management system powered by low-grade combustion waste heat running on steam ejectors for the first time. In this paper, an ejector operating at a low temperature under 100 °C for HEV's battery thermal management system is proposed and investigated. An in-house wet-steam model considering the condensation effect has been developed to characterise the ejector's internal flow structure and further analyse its feasibility as a thermal management system. The results show that the model considering the condensation process is more feasible in evaluating the performance of the steam ejector than the dry gas assumption. To improve the performance of the proposed ejector battery thermal management system, the effect of superheating of primary steam has been investigated. The results showed that an optimum point exists with 11 K superheating between improvement of entrainment ratio, the system's coefficient of performance and the power efficiency for the current case. The entrainment ratio at that point reaches around 0.45, while the coefficient of performance reaches 0.225.

A systematic analysis of the costs and environmental impacts of critical materials recovery from hybrid electric vehicle batteries in the U.S.

SummaryCritical materials such as rare earth underpin technologies needed for a decarbonized global economy. Recycling can mitigate the supply risks created by the increasing demand and net import dependence, and enable a circular economy for critical materials. In this study, we analyze the feasibility and life-cycle impacts of recovering critical materials from spent nickel metal hydride batteries from hybrid electric vehicles in the U.S., accounting for stocks, battery scrappage, and end-of-life reverse logistics, given uncertain future availability scenarios. Our results show that the total collection and recycling costs depend strongly on future battery availability, with marginal costs exceeding marginal revenues when the availability of spent batteries declines. We quantify the potential of recycling to reduce primary imports, as well as the accompanying climate change and resource impacts. We explore the underlying reverse logistics infrastructure required for battery recycling and evaluate strategies for reducing associated capital investment risk.

Dataset: A Systematic Analysis of the Costs and Environmental Impacts of Critical Materials Recovery from Hybrid Electric Vehicle Batteries in the U.S.

Dataset: A Systematic Analysis of the Costs and Environmental Impacts of Critical Materials Recovery from Hybrid Electric Vehicle Batteries in the U.S.

(Digital Presentation) Recycled Cathode Materials Enabled Superior Performance for Lithium-Ion Batteries

With the widely equipped Lithium-ion batteries (LIBs) in electronics and electric vehicles, proper handle spent LIBs has been the subject of increasing concern. Significantly raised concerns about resource constraints and environmental issues are brought by spent LIBs. Therefore, properly handling spent LIBs is urgent and necessary.[1] However, until now, getting manufacturers to recruit recycled materials has been a hard sell because recycled materials are deemed as inferior to commercial materials, which limits the development of recycling. Although previous publications stated that their recovered materials had a comparable performance as commercial materials, the results, based on coin cells and low electrode loading, cannot convince manufacturers to employ recycled materials in the new LIBs.[2] Here, we demonstrate that recycled cathode materials with optimized microstructure have the best industrial relevant testing results (up to 11Ah cells) so far and compare them with state-of-the-art commercial equivalent. Interestingly, the recycled materials not only pass all the aggressive industrial plug-in hybrid electric vehicle (PHEV) battery tests, but also outperform control counterparts in some tests. Specifically, 1 Ah cells with the recycled LiNi1/3Mn1/3Co1/3O2 have the best cycle life result reported for recycled materials and enable 4,200 cycles and 11,600 cycles at 80% and 70% capacity retention, which is 33% and 53% better than the state-of-the-art, commercial LiNi1/3Mn1/3Co1/3O2. Meanwhile, its rate performance is 88.6% better than commercial powders at 5C. Through detailed experimental and modeling analysis of pristine and cycled materials, we discover that the unique porous and larger inside void microstructure enables the superior rate and cycle performance and less phase transformation. Compared with the control sample, the surface area of the recycled LiNi1/3Mn1/3Co1/3O2 is 82.14% larger and the cumulative pore volume is 61.25% larger. Even some recycled particles have an outer diameter of the void space equal to 40% to 60% of the particle diameter. The unique microstructure can reduce 16% hoop stress during the discharge/charge process compared to control materials, and improve the lithium chemical diffusion coefficient, enabling the superior performance of cycle life and rate performance and less phase transformation. The results pave the way to re-introduce recycled materials into new batteries.[3] [1] M. Chen, X. Ma, B. Chen, R. Arsenault, P. Karlson, N. Simon, Y. Wang, Recycling End-of-Life Electric Vehicle Lithium-Ion Batteries, Joule 2019, 3, 2622.10.1016/j.joule.2019.09.014[2] X. Ma, L. Azhari, Y. Wang, Li-ion battery recycling challenges, Chem 2021.10.1016/j.chempr.2021.09.013[3] X. Ma, M. Chen, Z. Zheng, D. Bullen, J. Wang, C. Harrison, E. Gratz, Y. Lin, Z. Yang, Y. Zhang, F. Wang, D. Robertson, S.-B. Son, I. Bloom, J. Wen, M. Ge, X. Xiao, W.-K. Lee, M. Tang, Q. Wang, J. Fu, Y. Zhang, B. C. Sousa, R. Arsenault, P. Karlson, N. Simon, Y. Wang, Recycled cathode materials enabled superior performance for lithium-ion batteries, Joule 2021.10.1016/j.joule.2021.09.005

A triple threat: Ocean warming, acidification, and rare earth elements exposure triggers a superior antioxidant response and pigment production in the adaptable Ulva rigida

• La and Gd were accumulated in 24 h. • Elimination of La and Gd did not occur in U. rigida . • La and Gd showed different accumulation and elimination patterns in future predicted scenarios. • La and Gd triggered an efficient antioxidant defence response in U. rigida . • REE and climate change exposure requested a superior antioxidant response. Anthropogenic increased atmospheric CO 2 concentrations will lead to a drop of 0.4 units of seawater pH and ocean warming up to 4.8°C by 2100. Contaminant's toxicity is known to increase under a climate change scenario. Rare earth elements (REE) are emerging contaminants, that until now have no regulation regarding maximum concentration and discharge into the environment and have become vital to new technologies such as electric and hybrid-electric vehicle batteries, wind turbine generators and low-energy lighting. Studies of REE, namely Lanthanum (La) and Gadolinium (Gd), bioaccumulation, elimination, and toxicity in a multi-stressor environment (e.g., warming and acidification) are lacking. Hence, we investigated the algae phytoremediation capacity, the ecotoxicological responses and total chlorophyll and carotenoid contents in Ulva rigida during 7 days of co-exposure to La or Gd (15 µg L −1 or 10 µg L −1 , respectively), and warming and acidification. Additionally, we assessed these metals elimination, after a 7-day phase. After one day of experiment La and Gd clearly showed accumulation/adsorption in different patterns, at future conditions. Unlikely for Gd, Warming and Acidification contributed to the lowest La accumulation, and increased elimination. Lanthanum and Gd triggered an adequate activation of the antioxidant defence system, by avoiding lipid damage. Nevertheless, REE exposure in a near-future scenario triggered an overproduction of ROS that requested an enhanced antioxidant response. Additionally, an increase in total chlorophyll and carotenoids could also indicate an unforeseen energy expense, as a response to a multi-stressor environment.

Power Sharing Control Algorithm for Direct Integration of Fuel Cells in a Dual-Inverter Electric Vehicle Drivetrain

Fuel cell (FC)-battery hybrid electric vehicles (EVs) are an alternative to emergent battery EVs. Normally, a dc–dc converter is used to connect the low-voltage dc output of the FC to the high-voltage EV battery and ensure that a slow-changing unidirectional power flow is maintained from the FC. This article proposes to directly integrate the FC as one of the two energy sources in a dual-inverter-based EV drive. A conventional EV battery is used as the second energy source. A power sharing algorithm is introduced, which allows the dual-inverter drive to be modulated such that unidirectional power flow is maintained from the FC. In addition, power flow is controlled such that the minimum FC power to avoid its unnecessary shutdown is maintained under all operating points of an EV drive cycle, including regenerative braking. A key aspect of this algorithm is the injection of motor reactive current as a means to achieve desired FC power transfer during fast mechanical power reduction transients or when low mechanical power is required from the drive. This method allows for the motor to achieve fast mechanical power transients while respecting the slowly changing power reference of the FC.

Design of Solar and Battery Hybrid Electric Vehicle Charging Station

Microgrids have emerged as a new way to integrate and use large-scale distributed power to provide electricity to isolated locations, such as islands and villages. A utility engineering microgrid structure is designed to provide fast response and efficient operation by analyzing hybrid power systems and fuzzy-PI-based control techniques. This project aims to design a solar photovoltaic (PV)-battery-diesel generator-based hybrid power system employing a Sepic converter. The Perturb and Observe algorithm is used for maximum power extraction from solar panel. A lithium-ion battery is utilized as a storage battery in this system, which may be charged by the PV source. When the storage battery is depleted and PV generation is unavailable, the charging station uses the grid and a diesel generator. The generated voltage is synchronised at the Point of Common Coupling (PCC) to achieve continuous charging. The simulation is done in MATLAB/Simulink. The simulation output of using the PI control strategy is compared with the fuzzy-PI control strategy. This demonstrates the efficacy of the fuzzy-PI control method.