Battery-ultracapacitor Hybrid Energy Storage System Research Articles

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.

A Flexible Bidirectional Multiport Converter for Battery-Ultracapacitor Hybrid Energy Storage System in Electrified Vehicles

A Flexible Bidirectional Multiport Converter for Battery-Ultracapacitor Hybrid Energy Storage System in Electrified Vehicles

Aging-aware optimal power management control and component sizing of a fuel cell hybrid electric vehicle powertrain

The current study presents a comprehensive approach for optimizing the power distribution control and design of a Fuel Cell Hybrid Electric Vehicle (FCHEV) equipped with a Battery-Ultracapacitor Hybrid Energy Storage System (HESS) using a multi-objective evolutionary algorithm called interactive adaptive-weight genetic algorithm (i-AWGA). The method aims to maximize the vehicle’s driving range and the lifetimes of the fuel cell stack and battery while minimizing hydrogen fuel consumption and HESS size. The energy management strategy involves fuzzy logic controllers to distribute the power demand between the fuel cell and HESS and between the battery and ultracapacitor pack. Under the combined standardized cycle in which the optimization was developed, the optimized FCHEV configuration achieved a driving range of 444 km, hydrogen consumption of 0.9009 kg/100 km. Furthermore, the optimal configuration demonstrated robustness in real-world driving conditions, exhibiting improved energy efficiency, driving autonomy, and power sources lifespan. A cost–benefit analysis was also carried out, in which the optimized configuration was evaluated in terms of cost of ownership, achieving 31.28 US$/km, which means the substantial reduction of up to 63.59% in the invested cost-to-autonomy ratio as compared against other electrified vehicle powertrain topologies. Overall, this study offers a promising approach for designing efficient, cost-effective, and environmentally friendly FCHEVs with improved performance and durability.

Electric hydraulic hybrid vehicle powertrain design and optimization-based power distribution control to extend driving range and battery life cycle

This paper presents a comprehensive optimization procedure of a series electric hydraulic hybrid vehicle powertrain and control through the interactive adaptive-weight genetic algorithm method. The optimization simultaneously maximizes the driving range and battery lifespan, while minimizing onboard energy storage system mass. In this context, the design variables of the overall hydraulic drivetrain and the electric system were optimized. Moreover, a fuzzy-logic controller, which outputs the electric motor start-stop state and the torque applied to the pump which pressurizes the accumulator, is likewise considered in the formulation of the optimization problem to tune its membership functions, rules and weights. To ensure robust solutions, the electric–hydraulic vehicle was optimized under a combination of three standard driving cycles, which have antagonistic characteristics. Among the obtained optimum solutions, two configurations stood out with the driving range of 167.95 km and 199.73 km when using a battery of 320.23 kg and 384.12 kg and obtaining a battery life cycle of 53177 h and 46531 h, respectively. Moreover, these solutions were also tested under four different real-world driving cycles to evaluate their autonomy and performance, reaching driving ranges from 147 km in highway scenarios to over 262 km in urban cycles. The optimum configurations were compared with an also optimum electric vehicle powered by a battery-ultracapacitor hybrid energy storage system, obtaining a reduction of up to 9.57% in the ratio between powertrain cost and driving range. Finally, the optimization results indicate that electric hydraulic hybrid vehicle powertrain architectures can be a very attractive propulsion technology regarding both sustainable and economical aspects, effectively reducing battery aging by the use of a high power density hydraulic accumulator, which acts as a peak power buffer unit.

Battery-Ultracapacitor Hybrid Energy Storage System to Increase Battery Life Under Pulse Loads

This work presents a battery-ultracapacitor hybrid energy storage system (HESS) for pulsed loads (PL) in which ultracapacitors (UCs) run the pulse portion of the load while the battery powers the constant part of the load. Energy stored in UC depends upon the square of its voltage that’s why an active parallel hybrid topology with two bidirectional converters (BDC) is employed here that assigns UC a separate BDC to control its voltage between an upper and lower limit as per-requisite. The other converter is connected between the battery and DC-link to provide a distinct control of the battery. A MATLAB/Simulink model is developed to minimize the factors affecting the battery life such as capacity rate (C-rate), depth of discharge (DoD), and temperature rise due to PL by controlling the operating modes of the BDCs in which voltage of the UC and state of charge (SoC) of the battery are control variables. A constant output voltage across the load is maintained through feedback control. A detailed comparative analysis, among battery-alone, UC-alone, and battery-UC hybrid modes is performed which signifies that the proposed system is 55% lower in cost than its counterparts. The analysis shows that the proposed HESS reduces 50% capacity of a lead-acid battery, which is otherwise necessary to withstand the pulsating loads. Moreover, the performance of the HESS is also tested for electric vehicle (EV) load by hybridizing a high-voltage lithium-ion battery pack with a UC bank which confirms its suitability for EV applications.

Electric vehicle battery-ultracapacitor hybrid energy storage system and drivetrain optimization for a real-world urban driving scenario

A battery has normally a high energy density with low power density, while an ultracapacitor has a high power density but a low energy density. Therefore, this paper has been proposed to associate more than one storage technology generating a hybrid energy storage system (HESS), which has battery and ultracapacitor, whose objective is to improve the electric vehicle (EV) driving range. The HESS parameters have been evaluated in a configuration of EV powered by two in-wheel electric motors, coupled straight into the front wheels, and by a unique EM, connected to a differential transmission to drive the rear wheels. Moreover, this paper considers a real-world drive cycle based on the urban driving behavior of Campinas city, one of the most populous cities in Brazil. Aiming to minimize the HESS size and enhance the EV driving range, an optimization problem was formulated and solved using a genetic algorithm technique, in which the EV drivetrain parameters and HESS components and control are optimized. Finally, the obtained Pareto frontier defines the optimum EV configurations, in which the best-selected configurations were able to perform up to 188 km with a 418 kg HESS (maximum drive range solution), or 82.75 km with a 146.58 kg HESS (minimum HESS solution) and 319 km with a 188.43 kg HESS (best trade-off solution), without presenting performance losses.

Design of a Battery-Ultracapacitor Hybrid Energy Storage System with Power Flow Control for an Electric Vehicle

<p class="Abstract"><span lang="EN-MY">A combination of battery and ultracapacitor as a hybrid energy storage system (HESS) of an electric vehicle (EV) </span><span lang="EN-MY">can result in better acceleration performance, reduce battery charge-discharge cycle and longer driving range. This paper</span><span lang="EN-MY"> presents a </span><span lang="EN-MY">new converter design combining triple-half-bridge (THB) and buck-boost half-bridge (BHB) converters </span><span lang="EN-MY">in a battery-ultracapacitor HESS. The BHB converter is used to compensate the voltage variation of the ultracapacitor. </span><span lang="EN-MY">A power management system is proposed to control the power of battery and ultracapacitor to supply the demanded power. This paper describes the operation of the proposed converter using a simplified </span><span lang="EN-MY">∆</span><span lang="EN-MY">-type primary-referred equivalent circuit. This paper also shows the simulation results verifying the dynamic response of the proposed power management system for the proposed HESS. </span></p>

Design of High-Utilization Current-Sharing Controller for Battery-Ultracapacitor Hybrid Energy Storage System

In this paper, a new control strategy of battery-ultracapacitor hybrid energy storage system (HESS) is proposed for hybrid electric drive vehicles (HEVs). Compared to the stand, alone battery system may not be sufficient to satisfy peak demand periods during transients in HEVs, the ultracapacitor pack can supply or recover the peak power and it can be used in high C-rates. However, the problem of battery-ultracapacitor hybrid energy storage system (HESS) is how to interconnect the battery and ultracapacitor and how to control the power distribution. This paper reviewed some battery-ultracapacitor hybrid energy storage system topology and investigated the advantages and disadvantages, then proposed a new control strategy. The proposed control strategy can improve the system performance and ultracapacitor utilization, while also decreasing the battery pack size to avoid the thermal runaway problems and increase the life of the battery. The experiment results showed the proposed control strategy can improve 3% - 4% ultracapacitor utilization.

An Integrated Energy Management Strategy With Parameter Match Method for Plug-In Hybrid Electric Vehicles

A battery-ultracapacitor hybrid energy storage system (HESS) is a reliable candidate to overcome the drawbacks of a single power source system for its complementary features of power and energy in plug-in hybrid electric vehicle application. In this paper, an integrated energy management strategy procedure, which consists of three layers, is presented to distribute the power of the battery packs and the ultracapacitor packs. First, an HESS parameter match method is developed under different driving cycles with optimization objectives of cost and weight. Second, three-level wavelet transform (WT) algorithm is employed to isolate the different frequency components of power demand. The low-frequency components with less transients and sharps in power demand are provided by extending the battery lifetime, while an ultracapacitor satisfies the high-frequency components for its nice features of high specific power. Finally, fuzzy logic control is used to manage power flows between various components for guaranteeing the HESS performance. The simulation results show that compared with WT-based-only energy management strategy, the proposed procedure can always control the battery currents within 2C rate and reduce the energy consumption of about 6.54%.

Historical-Data-Based Energy Management in a Microgrid With a Hybrid Energy Storage System

In a microgrid, due to potential reverse power profiles between the renewable energy source (RES) and the loads, energy storage devices are employed to achieve high self-consumption of RES and to minimize power surplus flowing back into the main grid. This paper proposes a variable charging/discharging threshold method to manage the energy storage system. In addition, an adaptive intelligence technique (AIT) is put forward to raise the power management efficiency. A battery–ultra-capacitor hybrid energy storage system (HESS) with merits of high energy and power density is used to evaluate the proposed method with on-site-measured RES output data. Compared with the particle swarm optimization (PSO) algorithm based on the precise predicted data of the load and the RES, the results show that the proposed method can achieve better load smoothing and self-consumption of the RES without the requirement of precise load and RES forecasting.

Equivalent Series Resistance-Based Energy Loss Analysis of a Battery Semiactive Hybrid Energy Storage System

This paper provides a theoretical analysis on the energy loss of a battery–ultracapacitor hybrid energy storage system based on the equivalent series resistances and a pulsed current load profile. The optimal current distribution that minimizes the overall energy loss is proved to be solely determined by the ratio of internal resistances between battery and ultracapacitor packs. Due to a large difference in the internal resistances, a quasi-optimal current distribution can be considered to let the battery pack provide the average load current and ultracapacitor pack supply the entire dynamic load current. This result clearly demonstrates that the ultracapacitor pack should supply the most of the dynamic load current not only because of battery protection, but also for energy loss minimization. Finally, the theoretical analysis is validated by both simulation and experimental results. Additional discussions, such as sensitivity analysis, the influence of the sizing of ultracapacitors, and a realistic test cycle are also added for reference purposes.