Multi-source Bidirectional Quasi-Z-source Inverter using Fractional Order PI Controller for Electric Traction System

To solve the problem of battery capacity degradation caused by high current magnitudes and frequent current variations in electric vehicles (EVs), a hybrid energy storage system (HESS) incorporating high energy density storage (battery) and high power density storage (ultracapacitor) is proposed. The HESS contains a multi‐port DC‐DC converter, which controls the energy flow among the battery pack, the ultracapacitor (UC) pack, and the port of output. Considering the state of charge (SOC) of UC, the speed, and demand power of EV, an energy management strategy (EMS) based on fuzzy logic control (FLC) for the HESS is proposed in this paper. According to the strategy, the battery pack serves as a base power source while the UC pack serves as a transient power source so as to smooth the battery pack current and prolong the battery life. Simulation and experiment results illustrate the effectiveness of the proposed strategy for UC/battery HESS in EVs. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

A semi-active hybrid energy storage system, consisting of a Li-ion battery pack, dc/dc converter, and Li-ion capacitor pack was developed for a range extended plug-in vehicle. The vehicle has a series-parallel drivetrain with two electric motors, a gas engine, gearbox, and a clutch to allow the engine to run decoupled from the gearbox in range extending mode. The peak dc electrical requirement of the electric drivetrain is about 175 kW, which is similar to the peak power capability of the developed hybrid energy storage system. A model of the prototype hybrid energy storage system, which has the Li-ion capacitor pack connected directly to the motor drive's dc bus and the battery pack connected to the Li-ion capacitor pack via a dc/dc converter, is developed and used to determine the optimal power split between the battery and Li-ion capacitor packs and for tuning the developed real-time control system. The real-time control system is shown through modeling and experimental testing of the full scale hybrid energy storage systems to reduce battery pack losses, increase vehicle range, and to have performance approaching that of the optimal control solution as calculated via dynamic programming.

In conventional EV powertrain, Battery is the sole energy storage. This makes higher transient current stress on battery during operation, which shall reduce the life of the battery considerably. The size of the battery may required to be over rated to take care heavy transient demands with respect to its C rating. The regenerative braking capability of Electric power train is also gets limited due to the charging transient limitations in Battery. This eventually results in poor EV power train designs and vehicle performance. A hybrid energy storage system with Battery as primary energy source and Ultra capacitor as transient power manager is proposed as a viable solution. The combination shall provide better utilization of regenerative power, thus increase overall efficiency and range. The secondary storage can absorb all transients during regeneration and supply transients when required, during motoring. This can reduce the transient stress on battery increase the life. Cost effective Power train solution is also a major challenge in practical EV system design, and the proposed architecture is without dedicated DCDC converter for battery. The control logic can be formulated so as to meet the transient supplies of the load by ultra-capacitor and indirectly reduce that from battery. This provides low capacity DC-DC converter design, which could be appropriately overloaded for transient demands, thus reducing the overall size also. This can reduce the overall cost of EV power train. This paper discusses Hybrid Energy Storage System configuration for Battery Electric Vehicles and its Power Management strategy. The ultracapacitor is connected to DC bus using bidirectional converter in current control mode. Power split mechanism is implemented such that, drive current transient requirements, is met by the ultracapacitor depending on its voltage condition and remaining current requirement is met by battery.

This paper proposes a novel control scheme for hybrid energy storage system(HESS) in electric vehicles. The HESS above is composed of two parts: fuel cell (FC) and super capacitor(SC). FC, as the main power supply, provides a large proportion of required power. Whereas, SC, as the auxiliary energy storage source, plays a great role when starting and braking an electric vehicle(EV). Both energy sources are connected to the DC bus with DC-DC converters. Two converters share the very DC bus which is connected to the traction motor with a power inverter. To make sure the performances of the control system, a control method named generic model control is applied. Simulation results indicate the robustness and static performance of the proposed control scheme.

Vehicle electrification is gaining attention as a long-term option to overcome the effects of high carbon emissions and dependency on fossil fuels. To solve the issues of limited electric range and fluctuating power demands in electric vehicles (EVs), this study investigates the combination of a Hybrid Energy Storage System (HESS) with a Fuzzy Logic Controller. This method is a cutting-edge solution to addressing speed control difficulties in electric vehicles by combining a Fuzzy Logic Controller (FLC) with a Hybrid Energy Storage System (HESS). The combination of Fuzzy Logic, a sophisticated control approach capable of handling complex, nonlinear systems, and a HESS with numerous energy storage elements promises to transform how electric vehicles manage energy, respond to driver input, and maintain speed profiles. The HESS combines sophisticated battery technology and super capacitors to provide a synergistic approach to balancing energy density and fast power response. The fuzzy logic controller is designed to intelligently manage energy flow within the system, optimising the HESS's performance in real time in response to changing driving conditions, power requirements, and battery state. The study's goal is to demonstrate the usefulness of this integrated strategy in improving the overall energy economy, range, and performance of electric vehicles, which has been simulated and confirmed with MATLAB Simulink.

In recent years, the electric vehicles (EVs) power management strategy has been developed in order to reduce battery discharging power and fluctuation when an EV requires high and rapid discharging power due to frequent stop-and-go driving operations. A combination of lithium-ion battery and supercapacitor (SC) as the EV’s energy sources, known as a hybrid energy storage system (HESS), is a promising solution for fast discharging conditions. Effective power management to extensively utilize HESS can be developed if future power demand is accessible. A vehicular network as a typical form of the currently developed internet of things (IoT) has made future information obtainable by collecting information on surrounding data. This paper proposes a power management strategy for the HESS with the support of IoT. Since the obtained information from the vehicular network could not directly be used to improve HESS, a two-level control structure has been developed to perform future data prediction and power distribution. A fuzzy logic controller (FLC) is utilized in level one control structure to manage a HESS power split based on future information. Since FLC requires future information as a reference input, the future information is obtained by using an artificial neural network (ANN) in level two control structure. The ANN provides a direct prediction that could approximate the future power demand prediction with the assumption that the vehicular network scenario is deployed to obtain surrounding information. Simulation results demonstrate that the average discharging battery power and power variation are reduced by 46.1% and 52.3%, respectively, compared to the battery-only case.

The power management capabilities of hybrid energy storage systems offer several advantages for EVs, including improved performance, longer battery life, and reduced cost. HES technologies which combine Li-ion cells with ultra-capacitors (UC), are developing as just a possible alternative to the constraints of conventional ESS for conserving electricity. Lithium-ion batteries have a limited cycle life, meaning that they gradually degrade with each charge-discharge cycle. This can reduce the overall lifetime of the ESS and require more frequent replacements, adding to the cost and environmental impact of EVs. To circumvent those constraints, mixed battery packs combining battery technology as well as superconductors are being developed. To improve the design and control of hybrid energy storage systems for electric vehicles (EVs), a bi-level multi-objective framework has been proposed. A Bi-level multi-objective design and control framework, incorporating NSGA-II and fuzzy logic control, to obtain an optimal sized hybrid energy storage system and corresponding power management strategy. This dynamic and multi-objective architecture may achieve optimum solution hybrids battery bank as well as a matching significant power scheme one at a moment through integrating non-dominated sorting genetic and fuzzy logic control methods. To evaluate the effectiveness of the proposed bi-level multi-objective framework, Pareto optimal solutions of different hybrid energy storage systems are obtained. This approach can lead to significant improvements in the performance and efficiency of hybrid energy storage systems, making them more suitable for EVs and other applications that require high-performance energy storage solutions. Index Terms—Batteries, ultra capacitors, recursive imprecise input, non – linear and non optimisation, and e – mobility

The DC-DC converter crucial in modern power electronic systems. They are extensively used in renewable energy integration, hybrid electric vehicles (HEVs) and portable electronic devices. For the electric vehicles (EVs) with a hybrid energy storage system (HESS) that includes both battery and ultracapacitor (UC) efficient power management is the key feature for improving system performance, reliability, and the lifespan of the storage units. This paper mainly focusses on improving how energy is managed in electric vehicles (EVs) by the help of hybrid energy storage system (HESS) that includes-ultra capacitor, solar photovoltaic (PV) and battery input. Hear to control the flow of power which from the sources to EV load, I design a versatile non isolated multiport converter for integration of solar PV, battery and ultracapacitor and getting a single output. This paper suggests a non-isolated multi-input, single-output multiport converter design. This architecture aims to offer flexibility in the power flow while adjusting to the changing load demands of EVs. The DC-DC converter manages varying power demands by selecting the most appropriate energy source and channeling power through the proper paths depending on whether high or low current are required. It also supports bidirectional power transfer, enabling sources in both directions. Moreover, the converter allows single- stage power conversion, thereby improving the overall efficiency of the system.

Nowadays, most of the works are based on electric vehicle usage for sustainable transportation using traditional energy storage device, such as battery. Usage of batteries in electric vehicles is having several disadvantages, for example, life span, temperature, and charge estimation. In this paper, a novel control scheme for battery and supercapacitor‐ (SC‐) based hybrid energy storage system (HESS) using hybrid proportional and integral‐ (PI‐) sliding mode control (SMC) for electric vehicle (EV) applications is introduced and implemented. This HESS with hybrid controller proves the usage of batteries in EVs to its fullest potential. The conventional control strategy for HESS follows two‐loop voltage and current PI controllers with low‐pass filter (LPF) and involves tuning of multiple control parameters with variations of source and load disturbances. Performance of the system is affected by tuning PI controller constants. A slow response time with linear PI controllers is long which is not advisable for starting and sudden jerk conditions of EVs. Moreover, the PI controller performance is affected by the system parameter variations during load changes. And these parameters are dynamic in nature due to nonideal conditions. In this paper, a hybrid PI‐sliding mode controller (SMC) scheme is designed to control the bidirectional DC‐DC converters to overcome the drawbacks of aforementioned issues. The combined PI‐SMC controller reduces the tuning effort and reduces the effect of shift in operating point in controller performance. Linear modeling is done using small signal analysis for each subsystems. Permanent magnet synchronous machine (PMSM) is used as electric vehicle. The entire system and its controllers are simulated using MATLAB‐Simulink, and detailed comparison is carried between conventional PI and proposed hybrid PI‐SMC scheme to regulate the DC link voltage. The results are tabulated and show that the hybrid PI‐SMC scheme outperforms in transient and steady‐state conditions than the traditional PI controller. A scaled hardware prototype of 48 W set‐up is developed using dSPACE‐1104, and the experimental results have been carried out to verify the proposed system’s feasibility.

In order to complete the reasonable parameter matching of the pure electric vehicle (PEV) with a hybrid energy storage system (HESS) consisting of a battery pack and an ultra-capacitor pack, the impact of the selection of the economic index and the control strategy on the parameters matching cannot be ignored. This paper applies a more comprehensive total cost of ownership (TCO) of HESS as the optimal target and proposes an optimal methodology integrating parameters and control strategy for the PEV with HESS. Through the integrated optimal methodology, the application value of HESS is analyzed under various types of driving cycles and the results indicate that the HESS can significantly improve the economic performance of PEVs under both urban and suburban driving cycles. Due to the poor adaptability of traditional control strategies to different driving cycles, a novel extreme learning machine (ELM) based controller is established. Firstly, a dynamic programming (DP) based controller is applied for the offline optimization of the HESS power allocation under several typical driving cycles. Then, an analytical method combining correlation analysis and mean impact value (MIV) is employed to deal with offline sample data from DP and obtain the characteristic variables of the ELM model. Ultimately, the instantaneous power allocation strategy of HESS is acquired by utilizing ELM to learn offline data of HESS. Comparative simulations between the ELM-based controller and the rule-based controller are conducted, and the simulation results show that compared to the rule-based controller (RBC), the ELM-based controller reduces the electricity consumption by 3.78% and battery life loss by 6.51%.

This paper presents the modelling, design and power management of a hybrid energy storage system for a three-wheeled light electric vehicle under Indian driving conditions. The hybrid energy storage system described in this paper is characterized by effective coupling of Li-ion battery (primary energy source) and ultracapacitor (auxiliary source) interfaced with an efficient bi-directional converter. A design methodology related to vehicle modelling, choice of motor rating, converter design, sizing of Li-ion battery and ultracapacitor pack for the Indian driving cycle are presented. An improved real-time power-split management control strategy is proposed for proper power flow control of the hybrid energy storage system under various operating modes. The hybridized energy storage system with proposed control strategy improves the life of the battery and helps in effective utilization of the ultracapacitor. Furthermore, a relative comparison of the hybrid energy storage system with the battery energy storage system based on battery parameters and capital cost is also presented. Simulations are carried out in MATLAB/Simulink environment to verify the effectiveness of the proposed control strategy with modelled system components of three-wheeled light electric vehicle. A downscaled experimental prototype is built to validate the power-split between hybrid energy storage systems.

This paper addresses challenges related to the short service life and low efficiency of hybrid energy storage systems. A semiactive hybrid energy storage system with an ultracapacitor and a direct current (DC) bus directly connected in parallel is constructed first, and then related models are established for the lithium‐ion battery, system loss, and DC bus. Based on the multiobjective evaluation function, a hybrid energy storage system Model Predictive Control‐Differential Evolution (MPC‐DE) energy management method is proposed. Experiments were conducted under China Light‐Duty Vehicle Test Cycle‐Passenger Car (CLTC‐P) and Highway Fuel Economy Test (HWFET) driving cycles. The experimental results show that compared with other methods, this strategy can effectively reduce the loss of lithium‐ion battery and ultracapacitor hybrid energy storage system and improve the system efficiency.

A hybrid energy storage system (HESS) composed of electrochemical batteries and supercapacitors is considered. The supercapacitors aim to manage the peak power and thus increase the lifetime of the battery. A control scheme of this HESS is obtained by inversion of its energetic macroscopic representation. This control scheme enables different energy management strategies using a distribution input to share the energy between both devices. A switching strategy and a frequency strategy have been tested using this same control scheme. This flexible control scheme has been validated in real time by using a real HESS and a hardware‐in‐the‐loop simulation of the traction system of an electric vehicle.

Electric vehicles based on high-energy lithium-ion batteries often exhibit a substantial loss in performance at subzero temperatures: Due to slower electrochemical kinetics, the internal resistances of the batteries rise and diminish available power and capacity. Hybrid energy storage systems (HESSs) can be used to overcome these weaknesses. In this paper, the performance of two HESSs, combining a high-energy lithium-ion battery with either a high-power lithium-ion battery or a lithium-ion capacitor, has been investigated experimentally for a driving scenario at various temperatures. Both configurations enable driving at -20 °C, which was not possible without hybridization. The HESS using the high-power lithium-ion battery provides a substantially higher driving range due to its higher energy density. An analysis of different operating strategies has helped to maximize the driving range: Discharging the high-energy battery with a constant current and keeping the high-power cell at a higher state of charge (SoC) extend the driving duration, as the requested driving power can still be provided at a lower SoC of the high-energy battery. In addition to the HESSs, two energy storage systems without hybridization, consisting of different generations of high-energy lithium-ion cells, have been examined to disclose improvements in battery technology. These improvements narrow the benefits of HESSs, as the high-energy batteries have become less reliant on the support of an additional high-power device. Although HESSs lose importance for current lithium-ion battery systems, they can be a valuable option for next-generation lithium batteries, which are expected to provide higher energy densities but exhibit reduced rate capability.

The main purpose of this paper is to describe a novel power management control strategy for battery and supercapacitor hybrid energy storage system with the objective that the load power demand would be distributed into the energy storage devices in a way that each device can be utilized optimally. The paper describes the design and control of such an on-board hybrid energy storage system. The specific control strategy is designed based on a fuzzy-logic controller (FLC), which offers the advantage of flexibility and robustness and has been widely applied to the energy management or power distribution of multiple power sources system of an electric vehicle, to avoid the low efficiency operation region of every power source and achieve high efficiency without degrading the mechanism performance. However, prior experience fuzzy logic supervisory control is often difficulty in realizing the optimal distribution between battery and supercapacitor. In the presented research, an optimization method based on gold ratio cut-off rule is proposed to design the degree of hybridization (DOH) and membership functions of the fuzzy controller, to realize the optimal performance of fuzzy logic controller. The proposed optimization method is simple and effective, and can be applied to the practical hybrid system directly. Finally, the effectiveness of the proposed method was examined by using ADVISOR simulation platform, and in which the hybrid energy storage system was developed and embedded into the electric vehicle model. The model parameters were gained from the tested vehicle. Simulation results have shown that the proposed method can efficiently distribute the power between battery and supercapacitor to realize the design objective set forth.