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Energy storage systems play a crucial role in the overall performance of hybrid electric vehicles. Therefore, the state of the art in energy storage systems for hybrid electric vehicles is discussed in this paper along with appropriate background information for facilitating future research in this domain. Specifically, we compare key parameters such as cost, power density, energy density, cycle life, and response time for various energy storage systems. For energy storage systems employing ultra capacitors, we present characteristics such as cell voltage, cycle life, power density, and energy density. Furthermore, we discuss and evaluate the interconnection topologies for existing energy storage systems. We also discuss the hybrid battery–flywheel energy storage system as well as the mathematical modeling of the battery–ultracapacitor energy storage system. Toward the end, we discuss energy efficient powertrain for hybrid electric vehicles. [ABSTRACT FROM AUTHOR]

Energy storage systems play a crucial role in the overall performance of hybrid electric vehicles. Therefore, the state of the art in energy storage systems for hybrid electric vehicles is discussed in this paper along with appropriate background information for facilitating future research in this domain. Specifically, we compare key parameters such as cost, power density, energy density, cycle life, and response time for various energy storage systems. For energy storage systems employing ultra capacitors, we present characteristics such as cell voltage, cycle life, power density, and energy density. Furthermore, we discuss and evaluate the interconnection topologies for existing energy storage systems. We also discuss the hybrid battery–flywheel energy storage system as well as the mathematical modeling of the battery–ultracapacitor energy storage system. Toward the end, we discuss energy efficient powertrain for hybrid electric vehicles.

Compared with batteries, ultracapacitors have higher specific power and longer cycle life. They can act as power buffers to absorb peak power during charging and discharging, playing a role in peak shaving and valley filling, thereby extending the cycle life of the battery. In this article, a replaceable battery electric coupe SUV equipped with a lithium iron phosphate (LiFePO4) power battery is taken as the research object, and a vehicle dynamics simulation model is established on the MATLAB/Simulink platform. Parameter matching and control optimization for a hybrid energy storage system (HESS) are conducted. Through a proven semiempirical cycle model of the LiFePO4 power battery, the operating cycle life model is derived and used to estimate the battery cycle life. World Light Vehicle Test Cycle (WLTC) simulation results show that the HESS with 308 ultracapacitors can extend the cycle life of the LiFePO4 power battery by 34.24%, thus significantly reducing the operation cost of the battery replacement station. [ABSTRACT FROM AUTHOR]

Due to the continuous high traction power impact on the energy storage medium, it is easy to cause many safety risks during the driving process, such as triggering the aging mechanism, causing rapid deterioration of the battery performance during the driving process and even triggering thermal runaway. Hybrid energy storage is an effective way to solve this problem. The ultracapacitor is an energy storage device that has high power density, which can withstand high instantaneous currents and can be charged and discharged quickly. By combining batteries and ultracapacitors in a hybrid energy storage system, energy sources with different characteristics can be combined to take advantage of their respective strengths and increase the efficiency and lifetime of the system. The energy management strategy plays an important role in the performance of hybrid energy storage systems. Traditional optimization algorithms have difficulty improving the flexibility and practicality of applications. In this paper, an energy management strategy based on reinforcement learning is proposed. The results indicate that the proposed reinforcement method can effectively distribute the charging and discharging conditions of the power supply and maintain the SOC of the battery and, at the same time, meet the power demand of working conditions at the cost of less energy loss and effectively realize the goal of optimizing the overall efficiency and effective energy management strategy. [ABSTRACT FROM AUTHOR]

Compared with batteries, ultracapacitors have higher specific power and longer cycle life. They can act as power buffers to absorb peak power during charging and discharging, playing a role in peak shaving and valley filling, thereby extending the cycle life of the battery. In this article, a replaceable battery electric coupe SUV equipped with a lithium iron phosphate (LiFePO4) power battery is taken as the research object, and a vehicle dynamics simulation model is established on the MATLAB/Simulink platform. Parameter matching and control optimization for a hybrid energy storage system (HESS) are conducted. Through a proven semiempirical cycle model of the LiFePO4 power battery, the operating cycle life model is derived and used to estimate the battery cycle life. World Light Vehicle Test Cycle (WLTC) simulation results show that the HESS with 308 ultracapacitors can extend the cycle life of the LiFePO4 power battery by 34.24%, thus significantly reducing the operation cost of the battery replacement station.

In this paper, a battery pack and a supercapacitor bank hybrid energy storage system (HESS) with a new control configuration is proposed for electric vehicles (EVs). A bidirectional quasi-Z-source inverter (Bq-ZSI) and a bidirectional DC-DC converter are used in the powertrain of the EV. The scheme of the control for the proposed HESS Bq-ZSI using finite control set model predictive control (FCS-MPC) is first deduced to enhance the dynamic performance. With the idea of managing battery degradation mitigation, the fractional-order PI (FOPI) controller is then applied and associated with a filtering technique. The Opal-RT-based real-time simulation is next executed to verify the performance and effectiveness of the proposed HESS control strategy. As a result, the proposed HESS Bq-ZSI with this control scheme provides a quick response to the mechanical load and stable DC link voltage under the studied driving cycle. Moreover, the comparative results also show that the proposed HESS Bq-ZSI equipped with the new control configuration enables the reduction of the root-mean-square value, the mean value, and the standard deviation by 57%, 59%, and 27%, respectively, of the battery current compared to the battery-based inverter. Thus, the proposed HESS Bq-ZSI using these types of controllers can help to improve the EV system performance. [ABSTRACT FROM AUTHOR]

For plug-in hybrid electric vehicle (PHEV), using a hybrid energy storage system (HESS) instead of a single battery system can prolong the battery life and reduce the vehicle cost. To develop a PHEV with HESS, it is a key link to obtain the optimal size of the power supply and energy system that can meet the load requirements of a driving cycle. Since little effort has been dedicated to simultaneously performing the component sizing of PHEV and HESS, this paper proposes an approach based on the particle swarm optimization (PSO) algorithm to simultaneously determine the sizes of the engine, motor, battery and supercapacitor (SC) in a PHEV with HESS. The drivetrain cost is minimized in a different all-electric range (AER)—and depends on the battery type—while ensuring the driving performance requirements. In addition, the effects of the power system and drive cycle on the component sizes were analyzed and compared. The simulation results show that the cost of the PHEV drivetrain with the Ni-MH battery/SC HESS is reduced by up to 12.21% when compared to the drivetrain with the Li-ion battery/SC HESS. The drivetrain cost is reduced by 8.79% when compared to analysis-based optimization. The type of power supply system and drive cycle can significantly affect the optimization results. [ABSTRACT FROM AUTHOR]

Due to the continuous high traction power impact on the energy storage medium, it is easy to cause many safety risks during the driving process, such as triggering the aging mechanism, causing rapid deterioration of the battery performance during the driving process and even triggering thermal runaway. Hybrid energy storage is an effective way to solve this problem. The ultracapacitor is an energy storage device that has high power density, which can withstand high instantaneous currents and can be charged and discharged quickly. By combining batteries and ultracapacitors in a hybrid energy storage system, energy sources with different characteristics can be combined to take advantage of their respective strengths and increase the efficiency and lifetime of the system. The energy management strategy plays an important role in the performance of hybrid energy storage systems. Traditional optimization algorithms have difficulty improving the flexibility and practicality of applications. In this paper, an energy management strategy based on reinforcement learning is proposed. The results indicate that the proposed reinforcement method can effectively distribute the charging and discharging conditions of the power supply and maintain the SOC of the battery and, at the same time, meet the power demand of working conditions at the cost of less energy loss and effectively realize the goal of optimizing the overall efficiency and effective energy management strategy.

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. [ABSTRACT FROM AUTHOR]

In this paper, a battery pack and a supercapacitor bank hybrid energy storage system (HESS) with a new control configuration is proposed for electric vehicles (EVs). A bidirectional quasi-Z-source inverter (Bq-ZSI) and a bidirectional DC-DC converter are used in the powertrain of the EV. The scheme of the control for the proposed HESS Bq-ZSI using finite control set model predictive control (FCS-MPC) is first deduced to enhance the dynamic performance. With the idea of managing battery degradation mitigation, the fractional-order PI (FOPI) controller is then applied and associated with a filtering technique. The Opal-RT-based real-time simulation is next executed to verify the performance and effectiveness of the proposed HESS control strategy. As a result, the proposed HESS Bq-ZSI with this control scheme provides a quick response to the mechanical load and stable DC link voltage under the studied driving cycle. Moreover, the comparative results also show that the proposed HESS Bq-ZSI equipped with the new control configuration enables the reduction of the root-mean-square value, the mean value, and the standard deviation by 57%, 59%, and 27%, respectively, of the battery current compared to the battery-based inverter. Thus, the proposed HESS Bq-ZSI using these types of controllers can help to improve the EV system performance.

For plug-in hybrid electric vehicle (PHEV), using a hybrid energy storage system (HESS) instead of a single battery system can prolong the battery life and reduce the vehicle cost. To develop a PHEV with HESS, it is a key link to obtain the optimal size of the power supply and energy system that can meet the load requirements of a driving cycle. Since little effort has been dedicated to simultaneously performing the component sizing of PHEV and HESS, this paper proposes an approach based on the particle swarm optimization (PSO) algorithm to simultaneously determine the sizes of the engine, motor, battery and supercapacitor (SC) in a PHEV with HESS. The drivetrain cost is minimized in a different all-electric range (AER)—and depends on the battery type—while ensuring the driving performance requirements. In addition, the effects of the power system and drive cycle on the component sizes were analyzed and compared. The simulation results show that the cost of the PHEV drivetrain with the Ni-MH battery/SC HESS is reduced by up to 12.21% when compared to the drivetrain with the Li-ion battery/SC HESS. The drivetrain cost is reduced by 8.79% when compared to analysis-based optimization. The type of power supply system and drive cycle can significantly affect the optimization results.

The research presented in this paper documents the implementation of an active hybrid energy storage system that combined a battery pack and an ultracapacitor bank. The implemented hybrid energy storage system was used to reduce the peak-power that the battery needs to provide to the load. An active topology utilising two direct current/direct current (DC/DC) converters and a switch was used to implement the hybrid energy storage system. Fuzzy logic was used as a close-loop control structure to control the DC/DC converters in the topology, whilst a rule-based control strategy was used to control the operating states of the hybrid energy storage system. Experimental implementation of the system showed that the system was able to actively control the energy flow throughout the hybrid energy storage system in order to limit the power drawn from the battery to a user-defined limit. The performance of the fuzzy logic controllers was also experimentally found to be sufficient when used in conjunction with the rule-based control strategy. The system allows one to utilize batteries that are optimized for energy density seeing that the system was able to actively limit the power drawn from the battery, whilst providing the required power to the load by utilising the ultracapacitor bank.