Vehicle Energy Storage Research Articles

Developing an Innovative Seq2Seq Model to Predict the Remaining Useful Life of Low-Charged Battery Performance Using High-Speed Degradation Data

This study introduces a novel Sequence-to-Sequence (Seq2Seq) deep learning model for predicting lithium-ion batteries’ remaining useful life. We address the challenge of extrapolating battery performance from high-rate to low-rate charging conditions, a significant limitation in previous studies. Experiments were also conducted on commercial cells using charge rates from 1C to 3C. Comparative analysis of fully connected neural networks, convolutional neural networks, and long short-term memory networks revealed their limitations in extrapolating to untrained conditions. Our Seq2Seq model overcomes these limitations, predicting charging profiles and discharge capacity for untrained, low-rate conditions using only high-rate charging data. The Seq2Seq model demonstrated superior performance with low error and high curve-fitting accuracy for 1C and 1.2C untrained data. Unlike traditional models, it predicts complete charging profiles (voltage, current, temperature) for subsequent cycles, offering a comprehensive view of battery degradation. This method significantly reduces battery life testing time while maintaining high prediction accuracy. The findings have important implications for lithium-ion battery development, potentially accelerating advancements in electric vehicle technology and energy storage.

Research on mobile energy storage scheduling strategy for emergency power supply protection of post-disaster isolated loads

Aiming at the problem of insufficient power supply capacity of isolated loads in oceanic islands, a concept based on mobile energy storage and power conservation is proposed in this paper. Firstly, the energy flow and traffic flow characteristics of mobile energy storage are quantitatively analyzed, and an accurate mathematical model is established. Furthermore, the time-space distribution characteristics of load are carefully considered, and the Voronoi diagram and Tyson polygon method are introduced to determine cell differentiation. On this basis, combined with the power demand of load nodes and the energy storage characteristics of mobile energy storage vehicles, the evaluation indicators of cell energy and relative surplus energy of cells are designed, and the agent-cellular automata algorithm is used to schedule the mobile energy storage. The simulation results show that the power supply mode based on mobile energy storage can effectively improve the reliability of isolated loads. This paper provides a new perspective for solving the reliable power supply problem of isolated loads.

Large-scale energy storage for carbon neutrality: thermal energy storage for electrical vehicles

AbstractThermal Energy Storage (TES) systems are pivotal in advancing net-zero energy transitions, particularly in the energy sector, which is a major contributor to climate change due to carbon emissions. In electrical vehicles (EVs), TES systems enhance battery performance and regulate cabin temperatures, thus improving energy efficiency and extending vehicle range. The enhanced efficiency reduces overall energy consumption in EVs. Consequently, this reduction in energy demand can lead to decreased infrastructure needs, minimising the scale and investment required in energy production and distribution systems. Furthermore, the integration of TES with existing infrastructure allows for the simultaneous charging of thermal and electrical energy, leveraging waste heat or renewable energy sources. This not only cuts costs by optimizing resource use but also bolsters sustainability by minimising reliance on non-renewable energy sources. The widespread adoption of TES in EVs could transform these vehicles into nodes within large-scale, distributed energy storage systems, thus supporting smart grid operations and enhancing energy security. Strategic investments and regulatory updates are essential to realise a sustainable, carbon-neutral transportation future, underpinned by robust, cost-efficient infrastructure.

Thermal characterization of pouch cell using infrared thermography and electrochemical modelling for the Design of Effective Battery Thermal Management System

Lithium-ion (Li-ion) cells are the optimal choice of energy storage for battery electric vehicles. As the Li-ion cells must operate in a temperature range of 15–45 °C for optimal performance and life, thermal control of the cells is paramount. The study uses infrared imaging to investigate the temperature dynamics of two lithium-ion pouch cells with two energy capacities, 60 Ah and 20 Ah under different operational scenarios with natural convection. Experimental analysis conducted at ambient temperatures of 27 °C, 35 °C, and 40 °C reveals the necessity of cooling for the 20 Ah cell at temperatures exceeding 40 °C and C-rates beyond 1C. Similarly, thermal management is recommended for the 60 Ah cell at ambient temperatures of 35 °C and above, as temperatures surpass the 45 °C desirable threshold during charging and discharging rates of 1C and higher. Further studies are concentrated on the 60 Ah cell due to its increased cooling demands in comparison to the 20 Ah cell. The 60 Ah cell is modelled using NTGK and ECM electrochemical models and validated against experimental data, showing a good agreement within ±10% between both models and the experimental study. Finally, numerical simulations explain the efficacy of localized cooling with forced convection of liquid and performance metrics are introduced to evaluate the cooling efficiency of design configurations. Based on performance metrics, the T-shaped cold plate design, with an inlet coolant temperature of 30 °C, achieved the highest performance index of 1.13. This design notably decreased the maximum temperature and maximum temperature difference by 41.8% and 31.8%, respectively, compared to natural convection. This improvement is observed during the discharge of 60 Ah cell at 2C in a 40 °C environment, where the cell demands substantial cooling measures. Therefore, implementing localized cooling for the cell demonstrates its effectiveness in regulating temperature increase, leading to a significant decrease in the infrastructure requirements and weight of the thermal management system.

Tracking Degradation of Sulfolane-Based Electrolytes in 5 V LNMO Cells

Lithium-ion batteries (LIBs) are today the most used energy storage for portable electronics and electric vehicles – but are laden with concerns of materials scarcity and availability, alongside performance and cost. The development of high-voltage cobalt-free cathode active materials, such as LiNi0.5Mn1.5O4 (LNMO), represents one promising route for next-generation LIBs, due to a combination of high capacity and low cost. Combining LNMO with conventional LIB electrolytes, based on carbonate solvents, however, typically leads to severe capacity fading due to a range of different degradation mechanisms1, not the least due to the high electrochemical potential reached during charge, up to 5 V vs. Li+/Li°.Here we use a range of sulfolane-based electrolytes to improve the stability2 and track the degradation by intermittent current interruption (ICI), which is a rather new protocol to determine the internal resistance of battery cells during cycling3. The ICI data is presented in the form of heat maps, in order to better visualize the variation of internal resistance as function of the cell voltage. Furthermore, Raman spectroscopy is used to identify the decomposition products resulting from the degradation and the data is correlated with the ICI data. Overall this allows us to shine further light on the prospects of sulfolane-based electrolytes for 5 V cells as compared to conventional LIB electrolytes such as e.g. LP40.References Guo, K. et al. High‐Voltage Electrolyte Chemistry for Lithium Batteries. Small Science 2, 2100107 (2022).Xu, J. Critical Review on cathode–electrolyte Interphase Toward High-Voltage Cathodes for Li-Ion Batteries. Nano-Micro Letters vol. 14 (2022).Yin, L. et al. Implementing intermittent current interruption into Li-ion cell modelling for improved battery diagnostics. Electrochim Acta 427, (2022).

Energy management strategies in distribution system integrating electric vehicle and battery energy storage system: A review

AbstractThe electricity sector is witnessing a rise in renewable energy sources and the widespread adoption of electric vehicles, posing new challenges for distribution system. Additionally, the surge in carbon emissions resulting from industrialization and population growth continues to worsen global warming and climate change. In response, integrating electric vehicles (EVs) and battery energy storage systems (BESS) has emerged as a critical strategy, presenting both challenges and opportunities in effective energy management. BESSs offer potential solutions to mitigate these impacts. Furthermore, this review thoroughly explores issues related to lithium‐ion batteries, particularly in the context of EVs and energy management systems (EMS), identifies challenges, and provides recommendations for future research directions. The article concludes by outlining the current extent of investigation in the field of BESS and EV systems to provide researchers with a clear understanding. The escalation of carbon emissions stemming from industrialization and population expansion has worsened the effects of global warming and climate change. To address this challenge, the integration of Electric EVs and energy storage systems (ESS) has emerged as a pivotal strategy. This study examines optimization techniques, methodologies, and the evolving market landscape in distributed systems, with a focus on EVs and BESS. It also explores issues related to lithium‐ion batteries, particularly in the context of EVs and energy management systems. The article highlights the challenges and opportunities in the field of BESS and EV systems, emphasizing the need for ongoing research. BESSs offer potential solutions to mitigate these impacts. Moreover, it offers an extensive analysis of the existing BESS installations, outlining main areas of interest, pointing out difficulties, clarifying areas of unfinished study, and providing future directions.

An Integrated Self-Modularized Battery Equalizer and Supercapacitor Charger for Hybrid Electric Vehicle Energy Storage System

An Integrated Self-Modularized Battery Equalizer and Supercapacitor Charger for Hybrid Electric Vehicle Energy Storage System

Optimization of liquid cooled heat dissipation structure for vehicle energy storage batteries based on NSGA-II

Introduction: With the development of the new energy vehicle industry, the research aims to improve the energy utilization efficiency of electric vehicles by optimizing their composite power supply parameters.Methods: An optimization model based on non-dominated sorting genetic algorithm II was designed to optimize the parameters of liquid cooling structure of vehicle energy storage battery. The objective function and constraint conditions in the optimization process were defined to maximize the heat dissipation performance of the battery by establishing the heat transfer and hydrodynamic model of the electrolyzer.Results: The results showed that the optimization method had excellent performance on multiple evaluation indicators, the material degradation rate after optimization was reduced by 42%, the corrosion rate was reduced by 36%, and the battery life was increased by 17%. The optimization method ensured the maximum temperature control for the safe operation of the lithium-ion battery pack. The temperature of the battery pack was effectively controlled. The temperature difference was kept within 5°C, preventing the battery from overheating and extending its service life.Discussion: The proposed liquid cooling structure design can effectively manage and disperse the heat generated by the battery. This method provides a new idea for the optimization of the energy efficiency of the hybrid power system. This paper provides a new way for the efficient thermal management of the automotive power battery.

Perspectives on Advanced Lithium-Sulfur Batteries for Electric Vehicles and Grid-Scale Energy Storage.

Intensive increases in electrical energy storage are being driven by electric vehicles (EVs), smart grids, intermittent renewable energy, and decarbonization of the energy economy. Advanced lithium-sulfur batteries (LSBs) are among the most promising candidates, especially for EVs and grid-scale energy storage applications. In this topical review, the recent progress and perspectives of practical LSBs are reviewed and discussed; the challenges and solutions for these LSBs are analyzed and proposed for future practical and large-scale energy storage applications. Major challenges for the shuttle effect, reaction kinetics, and anodes are specifically addressed, and solutions are provided on the basis of recent progress in electrodes, electrolytes, binders, interlayers, conductivity, electrocatalysis, artificial SEI layers, etc. The characterization strategies (including in situ ones) and practical parameters (e.g., cost-effectiveness, battery management/modeling, environmental adaptability) are assessed for crucial automotive/stationary large-scale energy storage applications (i.e., EVs and grid energy storage). This topical review will give insights into the future development of promising Li-S batteries toward practical applications, including EVs and grid storage.

Many-objective optimization based mutual feed scheduling for energy system of integrated energy station

The battery storage system of the integrated energy station contains electric vehicles as the distributed energy storage node of the regional power system can help the regional power system to achieve peak shaving and valley filling. This paper proposes a many-objective optimization based mutual feed model of the energy system of the integrated energy station. The model includes multiple modules of electric vehicle (EV), wind turbine (WT), photovoltaic (PV) and battery energy storage system (BESS). BESS was modeled according to the two behaviors of EVs charging and swapping. The model ensures the role of peak shaving and valley filling for regional power, reduces the loss of battery life in the battery storage system of the integrated energy station, and reduces the operating cost of the battery storage system. In the optimized results, the regional power system load peak-to-valley ratio by 28.72 %, and the daily battery charge/discharge life loss is minimized to 0.00036 %. In addition, for known many-objective optimization problems, this paper also proposes a new meta-heuristic many-objective optimization algorithm the non-dominated sorting hunter prey optimization (NSHPO for short) and compared with other four many-objective optimization algorithms by three indexes (Distribution range, Hypervolume index, and Running time). The simulation results show that compared with other algorithms, the HV metrics of NSHPO are lower by about 1.56 %-7.26 %, Spread metrics are better by about 0.39 %-2.73 %, and the proposed NSHPO algorithm is better in terms of convergence and distributivity.

Optimized operation strategy for energy storage charging piles based on multi-strategy hybrid improved Harris hawk algorithm

In response to the issues arising from the disordered charging and discharging behavior of electric vehicle energy storage Charging piles, as well as the dynamic characteristics of electric vehicles, we have developed an ordered charging and discharging optimization scheduling strategy for energy storage Charging piles considering time-of-use electricity prices. The decision variables include the charging and discharging prices, states, and power of electric vehicles. We have constructed a mathematical model for electric vehicle charging and discharging scheduling with the optimization objectives of minimizing the charging and discharging costs of electric vehicles and maximizing the revenue of Charging piles. To address the challenges of multivariable, multi-objective, and high-dimensional optimization in the proposed model, we propose a Multi-strategy Hybrid Improved Harris Hawk Algorithm (MHIHHO). In addition, to validate the optimization performance of the proposed algorithm, CEC benchmark test functions are employed to assess the algorithm's optimization accuracy, convergence speed, stability, and significance. Finally, optimization-based scheduling simulations are performed considering power constraints for energy storage charging and discharging at different time intervals, as well as discharge loads. The proposed method reduces the peak-to-valley ratio of typical loads by 52.8 % compared to the original algorithm, effectively allocates charging piles to store electric power resources during off-peak periods, reduces user charging costs by 16.83 %–26.3 %, and increases Charging pile revenue.

Formula Student class electric vehicle energy storage - study and design assumptions

Formula Student class electric vehicle energy storage - study and design assumptions

Enhancing passive cell balancing techniques for electric vehicles

This research endeavors to enhance the efficiency and functionality of electric vehicle (EV) energy storage systems by scrutinizing the state of charge (SOC) across three batteries. Utilizing Matlab Simulink, a comprehensive battery management system (BMS) integrating passive cell balancing techniques has been meticulously devised. Additionally, a pioneering protective mechanism has been proposed to prevent undue discharges and overcharges, thus ensuring the longevity and reliability of EV battery systems. The study's findings underline the significance and applicability of the developed model in facilitating prospective enhancements within the realm of EV energy storage systems. By meticulously analyzing SOC dynamics across multiple batteries, the study offers valuable insights into optimizing battery utilization, prolonging battery lifespan, and enhancing overall system performance. Moreover, the integration of passive cell balancing techniques within the BMS framework represents a noteworthy advancement in battery management strategies. This approach not only promotes uniformity in SOC distribution among battery cells but also minimizes energy losses and mitigates the risk of cell degradation, thereby bolstering the operational efficiency and durability of EV energy storage systems. Furthermore, the proposed protective mechanism serves as a proactive measure to safeguard against potential damage caused by extreme operating conditions, such as overdischarge and overcharge events. By implementing real-time monitoring and control mechanisms, the protective system ensures the safe and reliable operation of EV batteries under varying driving conditions, thereby enhancing both safety and performance aspects. In conclusion, this research contributes to the ongoing efforts aimed at advancing the state-of-the-art in EV energy storage systems, holding immense potential for fostering innovation and driving sustainable progress in the field of electric mobility.

Enhancing the utilization of renewable generation on the highway with mobile energy storage vehicles and electric vehicles

The growth of electric vehicles (EVs) and renewable generation on the highway will magnify the imbalance between the energy supply and traffic electricity demand. Reshaping EV charging loads to address the above imbalance is challenging. Scheduling mobile energy storage vehicles (MESVs) to consume renewable energy is a promising way to balance supply and demand. Therefore, leveraging the spatiotemporal transferable characteristics of MESVs and EVs for energy, we propose a co-optimization method for the EV charging scheme and MESV scheduling on the highway, considering locational marginal price, renewable generation, and EV users’ benefit. Furthermore, by building a time–space–energy model for MESVs and EVs, we develop a bi-level optimization model to simulate the interaction between EV users and the highway operator (HO). In detail, the upper-level model optimizes EV charging pricing and MESV scheduling strategies to maximize HO profit, while the lower-level model considers EV users’ characteristics to minimize their charging-parking costs. The column-and-constraint generation algorithm is adopted due to the objective conflict between two levels. The study shows a 32.0% increase in renewable energy utilization and a reduction of 3190.1 kWh in electricity purchased from the main grid, promoting environmental and economic sustainability for the HO.

Circulating power flow restricted operation of the isolated bi-directional dual-active bridge DC-DC converter for battery charging applications

This paper presents the Non-Circulating Power Flow (NCPF) enabled Dual Active Bridge (DAB) converter controller strategy. The proposed DAB controller eliminates the non-circulating power flow for all the four conventional modulation algorithms, i.e., Single Phase Shift (SPS), Extended Phase Shift (EPS), Dual Phase Shift (DPS) and Triple Phase Shift (TPS) modulation schemes. This leads to lesser current stress and power loss across the DAB converter switches due to reduced peak inverse current. The proposed controller strategy finds various applications, such as in a Solid-State Transformer (SST) enabled microgrid, or for Electric Vehicle (EV) Energy Storage Unit (ESU: battery, supercapacitor) integrations, etc. The proposed NCPF operation reduces battery (and supercapacitor) current stress by upto 71.8 %, and improves operative environment, thus fostering its health and lifecycle. In NCPF mode, if the rating of installed switches (current and voltage ratings) remains unchanged, its power transfer capability is marginally reduced by mere 10.0 %. In addition to this, the operating range (i.e., controlling range of phase shift (Φ) and modulation index (m)) remains unaffected. This paper summarizes the DAB NCPF operation in restricted power flow mode as a function of Φ and m through 3-D and 4-D graphs. Finally, working of NCPF DAB is tested via both simulation and hardware platforms. Comparative results are shown for conventional and proposed modulation schemes on hardware platform, to showcase the elimination of circulating current in both forward and reverse DAB operating modes. It is inferred that proposed controller is worthy of real-world implementations.

Amorphous Cellulose Electrolyte for Long Life and Mechanically Robust Aqueous Structural Batteries

AbstractAqueous zinc (Zn)‐based structural batteries capable of both electrochemical energy storage and mechanical load‐bearing capabilities are attractive for next‐generation energy storage for future electric vehicles due to their eco‐friendliness, non‐toxic, and safe nature. However, parasitic free water activities plague aqueous Zn‐based batteries, detrimental to the electrochemical performance and longevity of the cell. Developing polymer gel electrolytes is a notable potential solution, but they usually have poor electrode interfacial interactions and inadequate mechanical properties. This article introduces a novel non‐fibrous highly amorphous cellulose polymer electrolyte “Cellyte” for aqueous structural Zn‐based batteries. Cellyte exhibits a high strength of ≈24 MPa and Young's modulus of ≈380 MPa, along with the ability to suppress parasitic water activity. The symmetric Zn||Cellyte||Zn cell therefore demonstrates excellent cycling stability of over ≈3000 h. Cellyte can also serve as the binder for the structural cathode material, creating a continuous polymer electrolyte–cathode interface, thereby increasing mechanical robustness and decreasing interfacial resistances of the battery, allowing the structural Zn||Cellyte||LMO‐CF battery to achieve high electrochemical performance with excellent cycling stability over 1200 h with ≈91.5% capacity retention. This provides a pathway to design mechanically robust, electrochemically performing, and safe structural batteries.

A Novel Reinforcement Learning Balance Control Strategy for Electric Vehicle Energy Storage Battery Pack

Abstract Energy imbalance in electric vehicle energy storage battery packs poses a challenge due to design and usage variations. Traditional balancing control algorithms struggle to cope with large-scale battery data and complex nonlinear relationship modeling, which jeopardizes the stability of energy storage systems. To overcome this issue, we propose a reinforcement learning (RL)-based strategy for battery pack balancing control. Our approach begins with adaptive battery pack modeling followed by the employment of an active balancing control strategy to determine the duration of the balancing charge state and rank the balancing strength of individual battery pack cells. Subsequently, a RL network is employed to learn dynamic parameters that capture battery pack variations, enabling subsequent automatic learning and prediction of effective balancing strategies while simultaneously selecting the optimal control policy. Our simulation experiments demonstrate that our approach ensures an orderly charge and discharge process of battery pack cells, achieving an impressive balance efficiency of 91% when compared to other similar balancing control methods. Furthermore, the optimization of RL methods results in significant improvements in battery pack energy efficiency, stability, and operational costs. Notably, our method also outperforms other similar control methods in terms of energy utilization rates, establishing its superiority in this category.

Benefit Evaluation of Carbon Reduction and Loss Reduction under a Coordinated Transportation–Electricity Network

With the extensive promotion of new energy vehicles, the number of electric vehicles (EVs) in China has increased rapidly. Electric vehicles are densely parked in garages, which means parking garages contain a large amount of idle energy storage resources. How to make this idle energy storage in garages participate in power system dispatch and evaluate the network loss and system carbon emissions considering electric vehicle energy storage has become an important research topic. The uncertainty around parking habits for electric vehicles causes it to be difficult to predict compared with the traditional energy storage system. Therefore, it is necessary to study its influence on the synergistic effect of loss reduction and carbon reduction as energy storage access. The benefits of new energy power generation output growth, energy waste reduction, and carbon emission reduction brought by loss reduction measures can be well reflected in the loss reduction index system of a power system in a low-carbon scenario. In this paper, a large amount of parking information in a certain area is collected, and the approximate parking habits of all vehicles in the simulated garage are obtained by the Monte Carlo method. Then, the load aggregation model is established, which is incorporated into the power system as an energy storage model. The synergy of loss reduction and carbon reduction is considered in this paper and comprehensively optimizes the strategy of integrating electric vehicles into the power system from the perspectives of electricity and carbon. In the scenarios of carbon flow calculation and network loss calculation, the YALMIP and CPLEX of MATLAB are applied, with various constraints input for simulation, so that the benefit evaluation method of carbon reduction and loss reduction under a coordinated transportation–electricity network is obtained.

An environmental perspective on developing dual energy storage for electric vehicles—a case study exploring Al-ion vs. supercapacitors alongside Li-ion

Much focus of dual energy-storage systems (DESSs) for electric vehicles (EVs) has been on cost reduction and performance enhancement. While these are important in the development of better systems, the environmental impacts of system and component-level choices should not be overlooked. The current interest in EVs is primarily motivated by environmental reasons such as climate change mitigation and reduction of fossil fuel use, so it is important to develop environmentally sound alternatives at the design stage. Assessing the environmental impact of developmental and mature chemistries provides valuable insights into the technologies that need to be selected now and which to develop for the future. This paper presents a cradle-to-gate (i.e., all raw material and production elements are considered; however, the “use” phase and recycling are not) lifecycle assessment of a DESS with Li-ion and aqueous Al-ion cells and that of one with Li-ion cells and supercapacitors. These are also compared to a full Li-ion EV battery in terms of their environmental impact for both a bus and car case study. Key findings show that the use of a DESS overall reduces the environmental impacts over the vehicle lifetime and provides an argument for further development of aqueous Al-ion cells for this application.

Coordinated Planning of EV Charging Stations and Mobile Energy Storage Vehicles in Highways With Traffic Flow Modeling

Coordinated Planning of EV Charging Stations and Mobile Energy Storage Vehicles in Highways With Traffic Flow Modeling