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 certain desired range. Second, a 30kW 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.

Taking driving condition ahead into consideration for proactive power management is one of the most important ways to improve the control performance of hybrid energy storage systems (HESSs). This paper presents a neural network based methodology for power demand prediction and a power distribution strategy for HESSs of pure electric vehicles. To develop an efficient prediction model, driving cycles are first grouped and distinguished as three different driving patterns. For each driving pattern, characteristic parameter data that could better featured driving cycles are extracted effectively and used to train neural network. The predictive information combined with its error is subsequently used for power distribution. Then, to deal with different dynamics of battery and supercapacitor systems, a frequency splitter is used and its frequency is further optimized by a particle swarm optimization algorithm to minimize the total cost including battery degradation and system energy for each driving pattern. Based on these efforts, a real-time predictive power management control strategy is finally proposed. To verify the effectiveness, simulation has been conducted to compare with the state-of-the-art control strategy under a speed profile composing of five standard driving cycles. Results show that obviously enhanced performance can be achieved by the proposed control strategy.

Terrain information can significantly impact load power demand, and in turn, on battery life and system efficiency of a hybrid energy storage system (ESS) with battery and supercapacitor. Taking terrain information ahead into consideration for proactive power management is one of the most important ways to improve battery life and overall system efficiency. However, since terrain information is typically available from commercial geographic information systems database, it is by nature inaccurate with uncertainties with respect to the requirements of power management. This is often worsening when combining with commercially low-quality global positioning systems. This paper proposes a novel power management strategy to cope with the inaccuracy and uncertainties of the terrain information with the aim to improve battery life, while maintaining overall system performance. First, the impact of terrain inaccuracy on battery life and system efficiency is analyzed based on two different hybrid ESSs with semiactive topologies. Then, a power management control strategy is developed that actively distributes the power between battery and supercapacitor with adaptation to terrain inaccuracy and uncertainties. The objective of the proposed power management control strategy is to minimize the total cost of the system, including the cost for battery life and energy. Finally, simulation is conducted that has verified the effectiveness of the proposed control strategy.

To ensure the starting torque ratio while improving the efficiency of the automatic transmission system, a power reflux hydro-mechanical transmission system which consists of a torque converter, a planetary gear, and two gearboxes is proposed. First, the properties of the speed ratio, torque ratio, efficiency, and capacity in the power reflux hydro-mechanical transmission system are modeled. Then, the non-dominated sorting genetic algorithm II is used to optimize the structural parameter of the planetary gear and the speed ratio of the gearbox T₁, with the speed ratio width in the high-efficiency area, efficiency, and power performance acting as target functions. Moreover, the method of selecting the specific torque converter for the power reflux hydro-mechanical transmission system is proposed. Results show that the starting torque ratio of the power reflux hydro-mechanical transmission system increases to 4.87 and the equivalent efficiency in high-efficiency area of the power reflux hydro-mechanical transmission system reaches to 90.87%. Therefore, the power reflux hydro-mechanical transmission system can reach higher efficiency while ensuring the starting torque ratio compared with hydro-mechanical power split transmission, which can significantly reduce fuel consumption once applied to the construction vehicle.

This paper introduces a game theory approach to implement power flow distribution mission for a fuel cell/battery hybrid system considering uncertain power information. To fully describe the vying interaction relationship between the fuel cell and the battery, we design the power distribution problem as a noncooperative game problem, in which the fuel cell and the battery are deemed to be two interactional players, and each one chooses proper amount of power supply to maximize its own optimization function relying on the other chosen. Different from all previous research work in the published papers, the power demand information of the adopted driving cycle is assumed to be absolutely known. In this paper, we discuss the case that when the power demand is uncertain how the players act and the Nash Equilibrium can be effectively achieved. Three original contributions are made. First, we develop the utility function for each player taking into account the uncertain behavior of the power demand due to inaccurate prediction of driving cycle. Second, an iterative algorithm with a fuzzy logical controller for correction is proposed to reduce the influence of uncertain power demand information on the decisions of the players. Finally, the effectiveness is validated by a comparison simulation test.