Frequency Power Sharing for Battery/Supercapacitors Hybrid Energy Storage System in Electric Vehicles

Energy management strategy (EMS) is a key issue for hybrid energy storage system (HESS) in electric vehicles. By innovatively introducing the current speed information, the vehicle speed optimized fuzzy energy management strategy (VSO-FEMS) for HESS is proposed in this paper. Firstly, the pruned fuzzy rules are formulated by the SOC change of battery and super-capacitor to preallocate the required power of vehicle. Then, the real-time vehicle speed is used to optimize the pre-allocated results based on the principle of vehicle dynamics, so as to realize the optimal allocation of required power. To validate the proposed VSO-FEMS strategy for HESS, simulations were done and compared with other EMSs under the typical urban cycle in China (CYC-CHINA). Results show that the final SOC of battery and super-capacitor are optimized in varying degrees, and the total energy consumption under the VSO-FEMS strategy is 2.43% less than rule-based strategy and 1.28% less than fuzzy control strategy, which verifies the effectiveness of the VSO-FEMS strategy.

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.

In this paper, a power-split strategy based on a real-time average power method is developed for improving power output of battery and mode switching frequency of a multi-mode hybrid energy storage system (HESS) in electric vehicles. To achieve mode switching and power distribution for the multi-mode HESS, a rule-based strategy is designed based on the high-frequency power demand. Furthermore, a simple real-time average power method is adopted to process the high-frequency power demand. Then, the real-time average power is used as a variable logic threshold value for the power-split strategy. Since the power-split controller responds to the smooth average power rather than the high-frequency power demand, the high-frequency mode switching of the multi-mode HESS can be avoided. The ultra-capacitor works as an enhanced low-pass power filter and the battery can supply smooth and steady output power to the motor inverter. Comparative simulations between the developed power-split strategy and the rule-based strategy are performed. The advantages of the developed power-split strategy for improving the power output of the battery and the mode switching frequency of the multi-mode HESS in electric vehicles are indicated under three typical driving cycles. Moreover, the longer time duration of the real-time average power is designed, the smoother power output of the battery and the lower mode switching frequency of the multi-mode HESS can be achieved.

In order to optimize the operation status of hybrid energy storage system in electric vehicles, a novel fuzzy logic control strategy is proposed. This strategy adopts Kalman filtering algorithm to estimates state of charge (SOC) and state of power (SOP), which can calculate the optimum power and alleviate the errors of SOC effectively. Besides, the framework of the strategy and the model of both battery and supercapacitor are introduced. The proposed control strategy will help to deal with energy storage system problem of relatively short lifespan and poor limited range in electric vehicles. So stability and efficiency of hybrid energy storage system (HESS) improve greatly.

The integration of ultracapacitors into the propulsion systems and implicitly into the hybrid energy storage systems (HESSs) of electric vehicles offers significant prospects for increasing performance, improving efficiency and extending the lifetime of battery systems. However, the realization of these benefits critically depends on the implementation of sophisticated control algorithms. From fundamental rule-based systems to advanced predictive and intelligent control strategies, the evolution and integration of these algorithms are driven by the need to efficiently manage the power flow, optimize energy utilization and ensure the long-term reliability of hybrid energy storage systems. This study briefly presents (in the form of a mini review) the research in this field and the development directions and application of state-of-the-art control algorithms, also highlighting the needs, challenges and future development directions. Based on the analysis made, it is found that from the point of view of performance vs. ease of implementation and computational resource requirements, fuzzy algorithms are the most suitable for HESS control in the case of common applications. However, when the performance requirements of HESSs relate to special and high-tech applications, HESS control will be achieved by using convolutional neural networks. As electric vehicles continue to evolve, the development of more intelligent, adaptive and robust control algorithms will be essential for achieving the full potential of integrating ultracapacitors into electric mobility.

Energy management of hybrid energy storage system in electric vehicle based on hybrid SCSO-RERNN approach

This paper deals with the Hybrid Energy Storage System (HESS) for Battery Electric, Hybrid and Plug-in Hybrid Electric Vehicles. Its performance is compared with conventional HESS design and also only Battery design, conventional design uses a bigger dc/dc converter between Battery and Ultracapacitor to satisfy the peak power demands in the real time, In this analysis a smaller dc/dc converter is used which maintains the voltage of ultracapacitor higher than the battery voltage by working as controlled energy pump. Battery will provide power directly only when voltage of the Ultracapacitor drops below the voltage of the Battery. Therefore, a constant and smooth load profile can be created for the battery. For the proposed HESS system Simulation results are presented to verify.

Hybrid electric vehicles (HEV) utilize electric power and a mechanical engine for propulsion; therefore, the performance of HEVs is directly influenced by the characteristics of the energy storage system (ESS). The ESS for an HEV generally requires high power performance, long cycle life, reliability and cost effectiveness; thus, a hybrid energy storage system (HESS) that combines different types of storage devices has been considered to fulfill both performance and cost requirements. To improve the operating efficiency and cycle life of a HESS, an advanced dynamic control regime in which pertinent storage devices in the HESS can be selectively operated based on their status is presented. Verification tests were performed to confirm the degree of improvement in energy efficiency. In this paper, an advanced HESS with a battery management system (BMS) that includes an optimal switching control function based on the estimated state of charge (SOC) is presented and verified.

Design and characterization of asymmetric supercapacitor useful in hybrid energy storage systems for electric vehicles

Energy management system plays a vital role in exploiting advantages of battery and supercapacitor hybrid energy storage systems in electric vehicles. Various energy management systems have been reported in the literature, of which the model predictive control is attracting more attentions due to its advantage in deal with system constraints. In this paper, a predictive energy management system is proposed based on a combination of Haar wavelet transform and model predictive control. Different from prior publications, the main contribution of this study is that the wavelet transform algorithm is introduced for power demand decomposition. At the same time, the power errors of the model predictive controllers are also fed to the wavelet transform algorithm for coefficient regulation. In this way, the power components distributed to the battery and supercapacitor can better match to their individual characteristics. The proposed method can reduce the maximum voltage drop of the battery up to 10.53%, 9.09% and 23.53%, the battery life cost up to 9.09%, 6.52% and 2.82%, respectively, as compared with the sole model predictive controller without wavelet transform based on NYCC, UDDS and NurembergR36 three driving cycles.

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.

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.

This paper concentrates on the design and control of a DC microgrid with PV, controlled Energy Storage System consists of battery, supercapacitor, DC loads, Electric Vehicle and Energy Management System (EMS). The main objective is to effectively manage the load requirements. A simple Adaptive energy management control is implemented. The system is simulated under different input and load condition in MATLAB/Simulink and the results are taken. It is observed from the obtained results that the dc microgrid with proposed energy management control satisfies the requirement under all conditions.

Compared with the conventional homogeneous electrical energy storage (EES) systems, hybrid electrical energy storage (HEES) systems provide high output power and energy density as well as high power conversion efficiency and low self-discharge at a low capital cost. Cycle efficiency of a HEES system (which is defined as the ratio of energy which is delivered by the HEES system to the load device to energy which is supplied by the power source to the HEES system) is one of the most important factors in determining the overall operational cost of the system. Therefore, EES banks within the HEES system should be prudently designed in order to maximize the overall cycle efficiency. However, the cycle efficiency is not only dependent on the EES element type, but also the dynamic conditions such as charge and discharge rates and energy efficiency of peripheral power circuitries. Also, due to the practical limitations of the power conversion circuitry, the specified capacity of the EES bank cannot be fully utilized, which in turn results in over-provisioning and thus additional capital expenditure for a HEES system with a specified level of service. This is the first paper that presents an EES bank reconfiguration architecture aiming at cycle efficiency and capacity utilization enhancement. We first provide a formal definition of balanced configurations and provide a general reconfigurable architecture for a HEES system, analyze key properties of the balanced reconfiguration, and propose a dynamic reconfiguration algorithm for optimal, online adaptation of the HEES system configuration to the characteristics of the power sources and the load devices as well as internal states of the EES banks. Experimental results demonstrate an overall cycle efficiency improvement of by up to 108% for a DC power demand profile, and pulse duty cycle improvement of by up to 127% for high-current pulsed power profile. We also present analysis results for capacity utilization improvement for a reconfigurable EES bank.

A hybrid electrical energy storage system (EESS) consisting of supercapacitor (SC) in combination with lithium-ion (Li-ion) battery has been studied through theoretical simulation and experiments to address thermal runaway in an electric vehicle. In theoretical simulation, the working temperature of Li-ion battery and SC has been varied from 0 to 75 °C in steps of 25 °C and the effect of the variation is observed on the output current, voltage and power of Li-ion battery and SC for a calculated load using the standard driving cycle. Further, electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV) have been performed to investigate the effect of temperature on SC. EIS measurements of SC indicate a low equivalent series resistance of 0.095 Ω at 25 °C. CV measurements indicate a high value of capacitance of 56.65 F/g at 25 °C for a scan rate of 5 mV s−1. Through theoretical simulation of EESS, a temperature increase ( $$ \Delta T $$ ) of 0.41 °C is calculated considering an initial peak of 10-s duration for the case of EESS without SC, whereas the use of SC reduces $$ \Delta T $$ to 0.025 °C. Further, optimal size calculation for hybrid EESS is done to achieve the least cost, wherein Li-ion battery capacity has been varied from 0 to 200 Ah and the SC capacity has been varied from 0 to 5 F in MATLAB simulation. The optimal combination is obtained as 30 Ah capacity of Li-ion battery and 3 F capacity of SC by connecting load across hybrid EESS for a duration of 3600 s.