Semi-active Hybrid Energy Storage System Research Articles

Energy Management Strategy Based on Model Predictive Control‐Differential Evolution for Hybrid Energy Storage System in Electric Vehicles

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.

Full Current-Type Control-Based Hybrid Energy Storage System

With greater power density, a hybrid power source that combines supercapacitors and batteries has a wide range of applications in pulse-operated power systems. In this paper, a supercapacitor/battery semi-active hybrid energy storage system (HESS) with a full current-type control strategy is presented. The studied HESS is composed of batteries, supercapacitors, and a bidirectional buck–boost converter. The converter is controlled such that supercapacitors supply load power pulses, and batteries provide the power in steady state. To realize the fast compensation of the supercapacitors to the load power pulses, a power distribution module based on hysteresis control theory is designed in the control system. Moreover, the control strategy does not require the model parameters of the converter and supercapacitors, so the control system is simplified. A complete configuration scheme and cost analysis of the proposed HESS are also presented. Obtained results show that the proposed supercapacitor/battery semi-active HESS has good performance in terms of dynamic response, weight, and energy utilization coefficient (EUC).

Multiobjective Optimization for a Li-Ion Battery and Supercapacitor Hybrid Energy Storage Electric Vehicle

The acceptance of hybrid energy storage system (HESS) Electric vehicles (EVs) is increasing rapidly because they produce zero emissions and have a higher energy efficiency. Due to the nonlinear and strong coupling relationships between the sizing parameters of the HESS components and the control strategy parameters and EV’s performances, energy consumption rate, running range and HESS cost, how to design the HESS EVs for different preferences is a key problem. How to get the real time performances from the HESS EV is a difficulty. The multiobjective optimization for the HESS EV considering the real time performances and the HESS cost is a solution. A Li-ion battery (BT) semi-active HESS and optimal energy control strategy were proposed for an EV. The multiobjectives include energy consumption over 100 km, acceleration time from 0–100 km per hour, maximum speed, running range and HESS cost of the EV. According to the degrees of impact on the multiobjectives, the scaled factors of BT capacity, the series number of Li-ion BTs, the series number of super-capacitors (SCs), the parallel number of SCs, and charge power of the SCs were chosen as the optimization variables. Two sets of different weights were used to simulate the multiobjective optimization problem in the ADVISOR software linked with MATLAB software. The simulation results show that some of the multiobjectives are sensitive to their weights. HESS EVs meeting different preferences can be designed through the weights of different objectives. Compared with the direct optimization algorithm, the genetic algorithm (GA) has a stronger optimization ability, and the single objective is more sensitive to its corresponding weight. The proposed optimization method is practical for a Li-ion BT and SC HESS EV design.

Parameter Matching Method of a Battery-Supercapacitor Hybrid Energy Storage System for Electric Vehicles

To satisfy the high-rate power demand fluctuations in the complicated driving cycle, electric vehicle (EV) energy storage systems should have both high power density and high energy density. In order to obtain better energy and power performances, a combination of battery and supercapacitor are utilized in this work to form a semi-active hybrid energy storage system (HESS). A parameter matching method of battery-supercapacitor HESS for electric vehicles (EVs) is proposed. This method can meet the performance indicators of EVs in terms of power and energy for parameter matching. The result shows that optimized parameter matching is obtained by reducing the weight and cost.

Real-Time Control of a Full Scale Li-ion Battery and Li-ion Capacitor Hybrid Energy Storage System for a Plug-in Hybrid Vehicle

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.

Experimental Study on a Semi-Active Battery-Supercapacitor Hybrid Energy Storage System for Electric Vehicle Application

This paper presents an experimental study on a semi-active hybrid energy storage system consisting of a battery pack and a supercapacitor pack for electric vehicle application. First, a real-time energy management control strategy based on a combination of filtering and fuzzy logic controller is proposed. The main advantage of the proposed control strategy is that the peak current of the battery can be obviously reduced while ensuring the voltage of the supercapacitor fluctuates within a certain desired range. Second, a 30 kW rated power experimental platform is constructed, in which only one dc/dc converter is used to regulate the power flow between the battery and the supercapacitor. Finally, a corrected battery fade model, which can accurately match the studied battery, is used to analyze the battery fade behavior. The results reveal that the battery capacity fade cost of the hybrid energy storage system can be reduced by 44.42%, 30.44%, and 57.16% compared with the sole battery storage under new European drive cycle, highway driving cycle, and Indian urban driving cycle, three driving cycles, respectively.

Semiactive Hybrid Energy Management System: A Solution for Electric Wheelchairs

Many disabled people use electric wheelchairs (EWs) in their daily lives. EWs take a considerable amount of time to charge and are less efficient in high-power-demand situations. This paper addresses these two problems using a semiactive hybrid energy storage system (SA-HESS) with a smart energy management system (SEMS). The SA-HESS contained a lithium-ion battery (LIB) and supercapacitor (SC) connected to a DC bus via a bidirectional DC–DC converter. The first task of the proposed SEMS was to charge the SA-HESS rapidly using a fuzzy-logic-controlled charging system. The second task was to reduce the stress of the LIB. The proposed SEMS divided the discharging operation into starting-, normal-, medium-, and high-power currents. The LIB was used in normal conditions, while the SC was mostly utilized during medium-power conditions, such as starting and uphill climbing of the EW. The conjunction of LIB and SC was employed to meet the high-power demand for smooth and reliable operation. A prototype was designed to validate the proposed methodology, and a comparison of the passive hybrid energy management system (P-HESS) and SA-HESS was performed under different driving tracks and loading conditions. The experimental results showed that the proposed system required less charging time and effectively utilized the power of the SC compared with P-HESS.

Power-split strategy based on average power method for semi-active hybrid energy storage system in small electric vehicles

In this paper, a simple power-split strategy based on an average power method is proposed for a semi-active hybrid energy storage system (HESS) in small electric vehicles. With the proposed power-split strategy based on the average power method, the supercapacitor (SC) in the semi-active HESS can act as an adaptive power filter. On this basis, the battery only needs to provide the constant/optimal power or compensation power to the motor inverter. Simulation model is established in Matlab/Simulink. Compared to the conventional power-split strategy based on real-time power demand, the operating current/power frequency and the current amplitude of the battery can be improved. In addition, the mode switch frequency of the semi-active HESS can also be improved. The SC can absorb extra output power from the battery or provide the peak power to the load. Therefore, the battery safety can be guaranteed and the battery cycle life can be effectively extended.

Research on energy management of hybrid energy storage system for electric bus

This article presents an energy management approach for the hybrid energy storage system in an electric bus at different temperatures. An electric bus equipped with a semi-active hybrid energy storage system is considered as the research object. According to the urban climatic characteristics of the vehicle running, the convex optimization approach considering temperature factors is proposed. The purpose of this study is to minimize the energy loss and maximize the discharge and charge depth of the super capacitor. Simulation results show that the comprehensive energy efficiency of the proposed method is 83.31% and the comprehensive energy efficiency is improved by 2.42% at 25°C, which is compared with the rule-based power allocation method based on urban driving cycle in Harbin, China. When the ambient temperature is −5°C, the comprehensive energy efficiency of the proposed method is 81.03%. This result is improved by 1.45% and the battery power variance at −5°C is reduced by 20.11% compared with the rule-based power allocation method. Therefore, the proposed power distribution method can effectively improve the functioning of an electric bus at different temperatures.

Adaptive Model Predictive Control-Based Energy Management for Semi-Active Hybrid Energy Storage Systems on Electric Vehicles

This paper deals with the energy management strategy (EMS) for an on-board semi-active hybrid energy storage system (HESS) composed of a Li-ion battery (LiB) and ultracapacitor (UC). Considering both the nonlinearity of the semi-active structure and driving condition uncertainty, while ensuring HESS operation within constraints, an adaptive model predictive control (AMPC) method is adopted to design the EMS. Within AMPC, LiB Ah-throughput is minimized online to extend its life. The proposed AMPC determines the optimal control action by solving a quadratic programming (QP) problem at each control interval, in which the QP solver receives control-oriented model matrices and current states for calculation. The control-oriented model is constructed by linearizing HESS online to approximate the original nonlinear model. Besides, a time-varying Kalman filter (TVKF) is introduced as the estimator to improve the state estimation accuracy. At the same time, sampling time, prediction horizon and scaling factors of AMPC are determined through simulation. Compared with standard MPC, TVKF reduces the estimation error by 1~3 orders of magnitude, and AMPC reduces LiB Ah-throughput by 4.3% under Urban Dynamometer Driving Schedule (UDDS) driving cycle condition, indicating superior model adaptivity. Furthermore, LiB Ah-throughput of AMPC under various classical driving cycles differs from that of dynamic programming by an average of 6.5% and reduces by an average of 10.6% compared to rule-based strategy of LiB Ah-throughput, showing excellent adaptation to driving condition uncertainty.

Primal-dual Interior-point Method based Energy Distribution Optimization for Semi-active Hybrid Energy Storage System

According to the complementary characteristics of batteries and supercapacitors, different semi-active hybrid energy storage systems using just one dc/dc converter have been exploited in hybrid electric vehicles. To minimize the amplitude of the current in battery and energy loss in the system, this paper proposes an optimization scheme for the semi-active hybrid energy storage system under the traction mode and the regenerative braking mode. The energy distribution optimization is formulated as a convex problem and the primal-dual interior-point method is leveraged to acquire the optimal solution. The simulation results validate that the proposed scheme is efficient to minimize the amplitude of the current in battery with a reduced energy loss.

Optimization of Battery Capacity Decay for Semi-Active Hybrid Energy Storage System Equipped on Electric City Bus

In view of severe changes in temperature during different seasons in cold areas of northern China, the decay of battery capacity of electric vehicles poses a problem. This paper uses an electric bus power system with semi-active hybrid energy storage system (HESS) as the research object and proposes a convex power distribution strategy to optimize the battery current that represents degradation of battery capacity based on the analysis of semi-empirical LiFePO4 battery life decline model. Simulation results show that, at a room temperature of 25 °C, during a daily trip organized by the Harbin City Driving Cycle including four cycle lines and four charging phases, the percentage of battery degradation was 9.6 × 10−3%. According to the average temperature of different months in Harbin, the percentage of battery degradation of the power distribution strategy proposed in this paper is 3.15% in one year; the electric bus can operate for 6.4 years until its capacity reduces to 80% of its initial value, and it can operate for 0.51 year more than the rule-based power distribution strategy.

Adaptive Sliding-Mode With Hysteresis Control Strategy for Simple Multimode Hybrid Energy Storage System in Electric Vehicles

In this paper, a simple multimode hybrid energy storage system (HESS) is proposed for electric vehicles (EVs). Compared to the improved semiactive HESS, only two switches are added in the main circuit topology of the multimode HESS, thereby achieving the operating modes can be actively switched. The mode switch strategy is designed according to the driving modes of the EV and the status of the power sources. To improve the overall system efficiency of the multimode HESS, the boost converter will operate at the peak efficiency to convey the energy from the battery to the supercapacitor (SC). An adaptive sliding-mode control (ASMC) with hysteresis control (HC) strategy is also developed by combining practical application of the multimode HESS. Simulations and experiments are presented to verify the effectiveness of the proposed multimode HESS and its ASMC strategy. Simulated results show that the multimode HESS can select suitable operating modes in corresponding conditions. Compared to the total sliding-mode control strategy, experimental results demonstrate that the ASMC with HC strategy can improve the operating stability of the multimode HESS under different operating modes. The multimode HESS can not only switch to suitable operating modes, but also avoid the excessive output power of the battery to meet different power demands of the load. In addition, the SC absorbs all the braking energy such that the battery safety can be effectively ensured.

Model predictive control for power management in a plug-in hybrid electric vehicle with a hybrid energy storage system

The fuel economy performance of plug-in hybrid electric vehicles (PHEVs) strongly depends on the power management strategy. This study proposes an integrated power management for a PHEV with multiple energy sources, including a semi-active hybrid energy storage system (HESS) and an assistance power unit (APU). The HESS consists of battery packs and ultracapacitor packs. In the integrated control strategy, the output power between the battery packs and ultracapacitor packs is regulated by the model predictive control strategy, while the output power between the APU and HESS is allocated by the rule-based strategy. In the model predictive control process, a period of the future velocity will be predicted, and the dynamic programming algorithm will be applied to optimize the control strategy accordingly. The robustness of the proposed approach is verified by three typical driving cycles, including the Manhattan cycle, CBDC cycle and UDDSHDV cycle. The results show that the proposed control strategy can promote fuel economy compared with the original control strategy, especially in the charge sustain mode under the MANHATTAN driving cycle (21.88% improvement).

A novel fuzzy-logic based control strategy for a semi-active battery/super-capacitor hybrid energy storage system in vehicular applications

A hybrid energy storage system (HESS) composed of a battery and super-capacitor (SC) can make full use of advantages in energy and power density, which can further improve the performance of hybrid electric vehicles (HEV). Most studies have been limited to configuration and control to reduce the burden and prolong the life cycle of the battery, seldom focusing on the redesign of energy management strategies (EMS) for HEVs when passing from battery-only energy storage to HESS. In this paper, the equivalent fuel consumption during parallel-charging of a semi-active HESS in a series-parallel HEV under rule-based EMS was deduced, and a fuzzy-logic based (FLB) CS for the HESS was established using the state of energy (SOE) of the battery and SC as inputs. This allows the requirement of energy balance for HESS to be clearly expressed. Furthermore, the regulated EMS for HEV, which could dynamically manage the high efficiency of the engine and maintain the real-time charging/discharging capability of HESS, was proposed and verified under hardware in a loop test (HIL). Quantitative comparison of results between HESS and battery-only indicated that velocity-based SC'SOE-adjustable EMS presented in this paper could make better use of SC in filtering than SOE-constant EMS, and improve the fuel economy of HEVs from 22.76 l/100 km (battery-only) to 21.18 l/100 km. In order to emphasize the advantages of management, the electric energy usage/loss and efficiency under city driving-cycle were also presented.

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.

A New Method to Optimize Semiactive Hybrid Energy Storage System for Hybrid Electrical Vehicle by Using PE Function

Although both battery and super-capacitor are important power sources for hybrid electric vehicles, there is no accurate configuration theory to match the above two kinds of power sources which have significantly different characteristics on energy and power storage for the goal of making good use of their individual features without size wasting. In this paper, a new performance is presented that is used for analysis and optimal design method of battery and super-capacitor for hybrid energy storage system of a parallel hybrid electrical vehicle. In order to achieve optimal design with less consumption, the power-energy function is applied to establish direct mathematical relationship between demand power and the performance. During matching process, firstly, three typical operating conditions are chosen as the basis of design; secondly, the energy and power capacity evaluation methods for the parameters of battery and super-capacitor in hybrid energy storage system are proposed; thirdly, the mass, volume, and cost of the system are optimized simultaneously by using power-energy function. As a result, there are significant advantages on mass, volume, and cost for the hybrid energy storage system with the matching method. Simulation results fit well with the results of analysis, which confirms that the optimized design can meet the demand of hybrid electric vehicle well.

A comparison study of different semi-active hybrid energy storage system topologies for electric vehicles

In this paper, four different semi-active hybrid energy storage systems (HESSs), which use both supercapacitors (SCs) and batteries, are compared based on an electric city bus running the China Bus Driving Cycle (CBDC). The SC sizes of the different HESS topologies are optimized by using the dynamic programming (DP) approach, based on a dynamic degradation model of the LiFePO4 battery. The operation costs of different HESSs, including the electricity and the battery degradation costs over a whole CBDC, are minimized in the optimization process. Based on the DP results, near-optimal control strategies of different HESSs for on-line uses are proposed. Finally, the four HESS topologies are comprehensively compared from different aspects, including operation cost, initial cost, and DC bus voltage variation. Simulation results show that all HESS topologies have their merits and drawbacks, and can be used in different applications with different requirements. In addition, about 50% of the operation cost of the energy storage system is reduced by the semi-active HESSs when compared to the battery-only topology. Thus the effectiveness of adopting the SC in the HESS is verified.