Electric Vehicle Energy Storage Research Articles

Power management system for a hybrid energy storage electric vehicle using fuzzy logic controller

This research work introduces a power management system for a hybrid energy storage system (PMHESS) configuration for urban electric vehicles utilizing a fuzzy logic controller (FLC). Consequently, the configuration includes two DC/DC interleaved converters that establish a connection between the battery and ultra-capacitors (UCs), thereby ensuring a substantial power capacity. The FLC is adaptable and sturdy, considering information from sources other than the vehicle, such as other vehicles or road infrastructure. The study explores incorporating road topography into the control structure to improve hybrid storage performance. Simulation results show that combining lithium batteries with UCs improves the energy source’s performance and reliability. The power management algorithm reduces sudden demands on the battery by considering the slope of the ground. The work proposes energy storage integration for electric vehicles, exploring its benefits through simulations. Overall, the proposed hybrid energy storage system (HESS) demonstrates high efficiency and power for urban electric vehicles. The results are validated using MATLAB/Simulink.

The influence of ripple on DC energy measurement and the improvement of wavelet threshold function filtering method

DC energy has been applied in the construction of DC power grids, photovoltaic power generation, energy storage, and rapid charging of electric vehicles. The application of DC energy is of great significance for energy conservation, emission reduction, carbon reduction, and efficiency increase. In this paper, the reasons for the decrease in DC energy measurement accuracy caused by ripples in voltage and current are analyzed. The minimum requirements of ripples for DC energy meters are analyzed, and the traditional filtering method based on wavelet threshold is studied. An optimized and fast iterative wavelet threshold function was designed, the current signal of electric vehicle charging was selected, and the effectiveness of the algorithm proposed in this paper was verified through simulation.

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.

Particle Swarm-Optimized Fuzzy Logic Energy Management of Hybrid Energy Storage in Electric Vehicles

A lithium-ion battery–ultracapacitor hybrid energy storage system (HESS) has been recognized as a viable solution to address the limitations of single battery energy sources in electric vehicles (EVs), especially in urban driving conditions, owing to its complementary energy features. However, an energy management strategy (EMS) is required for the optimal performance of the HESS. In this paper, an EMS based on the particle swarm optimization (PSO) of the fuzzy logic controller (FLC) is proposed. It aims to minimize battery current and power peak fluctuations, thereby enhancing its capacity and lifespan, by optimizing the weights of formulated FLC rules using the PSO algorithm. This paper utilizes the battery temperature as the cost function in the optimization problem of the PSO due to the sensitivity of lithium-ion batteries (LIBs) to operating temperature variations compared to ultracapacitors (UCs). An evaluation of optimized FLC using PSO and a developed EV model is conducted under the Urban Dynamometer Driving Schedule (UDDS) and compared with the unoptimized FLC. The result shows that 5.4% of the battery’s capacity was conserved at 25.5 °C, which is the highest operating temperature attained under the proposed strategy.

Experimental Study of the Liquid Cold Plate Cooling System for Lithium-Ion Battery in Electric Vehicle

The battery remains the primary choice of energy and power storage in electric vehicles. To optimize battery performance, manufacturers have developed various technologies, including the battery thermal management system (BTMS). In this study, an experimental investigation was conducted to determine the cooling performance and characteristics of a liquid cold plate cooling system for 18650 lithium-ion battery. The testing involved an 18650 SONY 2100 mAh lithium-ion battery and a 400 W DC Load Tester. Testing was carried out by discharging the battery module and taking data on battery temperature and working fluid temperature to get the heat transfer coefficient. The discharge current varied at 1.25 C, 1.5 C, 1.75 C, and 2 C, while the flow rate was set at 0.2 LPM, 0.5 LPM, and 0.8 LPM. The results indicate that the cooling rate increases as the flow rate rises. The battery temperature rises quicker near the negative pole than at the further location. Furthermore, the pressure drop on the liquid cold plate increases as the flow rate rises, while the highest heat transfer coefficient to pressure drop ratio is observed at 0.2 LPM.

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.

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.

Hybrid battery energy storage for light electric vehicle — From lab to real life operation tests

The aim of the research presented in the paper is to improve the lifetime of lead-acid battery systems which are widely used in low-speed electric vehicles or utility vehicles, since the cycle life of lead-acid batteries is nonlinearly dependent on the depth of discharge and electrolyte temperature. The solution proposed here is to connect lead-acid batteries with a small lithium‑iron-phosphate battery using only a diode. This connection is simple and does not require the use of an actively controlled converter. The concept was tested at various levels of an experiment, from the initial calculation of cycle life improvement and simulation, through a laboratory experiment, all the way to operational environment tests. The results obtained confirmed the main assumptions of the study: decrease in the depth of discharge of lead-acid battery for the distance traveled of around 17 %—which would translate into a 30 % better cycle life according to the characteristics indicated by the producers—and decrease in the root mean square and maximum current values of lead-acid battery by around 40–50 % and 26 %, respectively. Range extension was also achieved in vehicles. The solution has been successfully introduced to the market and is now used in few light electric vehicles.

Dynamic load prediction of charging piles for energy storage electric vehicles based on Space-time constraints in the internet of things environment

Abstract This paper puts forward the dynamic load prediction of charging piles of energy storage electric vehicles based on time and space constraints in the Internet of Things environment, which can improve the load prediction effect of charging piles of electric vehicles and solve the problems of difficult power grid control and low power quality caused by the randomness of charging loads in time and space. After constructing a traffic road network model based on the Internet of Things, a travel chain model with different complexity and an electric vehicle charging model, the travel chain is randomly extracted. With the shortest travel time as a constraint, combined with the traffic road network model based on the Internet of Things, the travel route and travel time are determined. According to the State of Charge (SOC) and the travel destination, the location and charging time of the energy storage electric vehicle charging pile are determined. After obtaining the time-space distribution information of the energy storage electric vehicle charging pile at different times and in different regions, it is used as the input of the deep multi-step time-space dynamic neural network, and the network output is the dynamic electric vehicle charging pile. The experimental results show that this method can realize the dynamic load prediction of electric vehicle charging piles. When the number of stacking units is 11, the indexes of Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) are the lowest and the index of R 2 is the largest. The load of charging piles in residential areas and work areas exists in the morning and evening peak hours, while the load fluctuation of charging piles in other areas presents a decentralized change law; The higher the complexity of regional traffic network, the greater the load of electric vehicle charging piles in the morning rush hour.

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.

Enhancing Low-Pass Filter Energy Management with Adaptive State of Charge Limiter for Hybrid Energy Storage in Electric Vehicles

Enhancing Low-Pass Filter Energy Management with Adaptive State of Charge Limiter for Hybrid Energy Storage in Electric Vehicles

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

Dual Electrolyte Design for Lithium-Ion Batteries

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. 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 their 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 wide electrochemical stability window (ESW) needed for a graphite//LNMO cell.Here we use the rather novel route to design LIB cells by utilizing two different electrolytes to together cover the full ESW2. The separation of the two electrolytes is assured by designing them to be immiscible, either chemically or more mechanically, while for the functionality of the cell we also monitor the evolution of a stable solid electrolyte interphase (SEI) at the anode side and a stable cathode electrolyte interphase (CEI) at the cathode side. This proof-of-concept study allows us to shine light on the best ways to design a dual electrolyte cell.References K. Guo et al. , “ High‐Voltage Electrolyte Chemistry for Lithium Batteries ”, Small Science 2, 2100107 (2022). S. He et al. , “ Design of a Dual-Electrolyte Battery System Based on a High-Energy NCM811-Si/C Full Battery Electrode-Compatible Electrolyte ”, ACS Appl Mater Interfaces 13, 54069–54078 (2021).

Development of Electro-Thermal Model for Air Cooling Study of Electric Vehicle Lithium-Ion Battery Module Operating in Indian Conditions and Drive Cycle

Lithium-ion cells have higher energy density at a specific power, making them a primary choice for battery packs in energy storage of modern technology, powering from smartphones to Electric vehicle (EV) and grid-scale energy storage systems. However, their performance, safety and lifespan are temperature sensitive and developing a fail-safe mechanism is considered as a pressing priority. In the present study, a 2D electro-thermal model is developed in MATLAB to characterize the thermal behaviour of cylindrical cells (individual and inline) during discharge. The thermal model solves the transient heat conduction equation with internal heat generation. The model is validated with the available experimental data for a single cell at a constant discharge rate and found to be in good agreement within 0.5°C. The analysis is extended to a battery module with 6 cell-inline configuration and validated. This validated model is used to study the impact of multiple cells (1-10) in inline configuration. For Indian drive cycle analysis, the current profile is estimated based on the drive cycle and its impacts on cell temperature rise in battery module is studied when subjected to variation in monthly average ambient temperature. Battery module maximum temperature rises to 60 °C and Δ T (Tmax - Tmin) across cells is 8.8 °C during summer conditions under natural convection due to multiple drive cycle loading. The battery module temperature can be kept under safe operating limits (Tmax≤ 45 °C, Δ T ≤ 4 °C) with forced convection of 2 m/s and 25 °C. The study aims to provide insights into the thermal behaviour of Lithium-ion cells that can be used to design safe and cost-effective air-cooled thermal management system for battery module.

Faults Diagnosis for Large-Scale Battery Packs via Texture Analysis on Spatial–Temporal Images Converted From Electrical Behaviors

Battery failures have become the most intractable obstacles undermining the market confidence in applications like electric vehicle and power grid energy storage. This paper aims to fashion a generic diagnosis scheme against the faults in large-scale battery systems. First, a voltmeter array-based anomaly perception mechanism against the electrical behaviors of battery packs is developed. Then, system information on spatial arrangement and temporal dynamics is organically fused and drawn as a kind of pseudo 2D images (P2Is). Afterwards, by analyzing the resultant P2Is with the 2D variational mode decomposition (2D-VMD) and gray level co-occurrence matrix (GLCM), some statistical quantities concerning multi-scale texture features, extracted and refined by the principal component analysis (PCA), are found to have strong indicative associations with battery fault type and fault grade. Finally, relying on the multi-class relevance vector machine (M-RVM), feature evidences are synthesized to detect fault occurrence and give judgements on fault specifics of type and severity. Experimental verifications on a li-ion battery pack with 180 cells suggest that the proposed scheme behaves well in fault type isolating, with an accuracy rate of 97.6% and in fault severity grading, with an accuracy rate of 84.67%.

Integrated Optimal Energy Management and Sizing of Hybrid Battery/Flywheel Energy Storage for Electric Vehicles

This paper presents an integrated optimal Energy Management Strategy (EMS) and sizing of a high-speed Flywheel Energy Storage System (FESS) in a battery electric vehicle (EV). The methodology aims at extending the battery cycle life and drive range by relegating fast dynamics of the power demand to the FESS. For the EMS, the battery power and FESS energy are considered as weighted objectives of an optimization problem that are established using Pontryagin's Minimum Principle (PMP). In order to derive the optimal FESS size, the sizing algorithm is targeted to minimize the battery degradation level and increase the FESS energy interaction with the system. The performance of the proposed methodology is assessed using some driving cycles including ECE-15, UDDC, HWFET, and US06. Comparing the proposed battery/FESS to the battery-only topology, the battery SoH and life cycle are improved considerably for different driving cycles.