An Energy Management Strategy for Hybrid Energy Storage Systems coordinate with state of thermal and power. (May 2022)

• Record Type: Journal Article
• Authors: Wang, Li; Li, Mince; Chen, Zonghai
• Is Part Of: Control engineering practice. Volume 122(2022)
• Publication Date: 2022-05
• Subjects: Energy management strategy -- State of power -- Electro-thermal model -- Power predictive methods -- Adaptive model predictive control; Automatic control -- Periodicals; 629.89
• DOI: 10.1016/j.conengprac.2022.105122
• Languages: English
• ISSNs: 0967-0661

Abstract

Abstract: Hybrid Energy Storage Systems (HESS) are playing an increasingly important role in the process of electric vehicles and the HESS Energy Management Strategy (EMS) must achieve optimal power distribution results while guaranteeing the safe operation of the energy storage units. The state of power of batteries and supercapacitors (SCs) is the key to their proper operation. In this paper, we proposed an EMS for HESS by considering power and current limits. The electro-thermal model and power predictive methods of HESS are detailed in the paper. The main body of the EMS is the adaptive model predictive control algorithm with an electrical prediction model and a thermal model which are mainly used to vary the various parameters in the prediction model. The power predictive method takes into account the various influences of temperature, voltage, current and state of charge. These multi-limits are applied to constrain the charge and discharge currents that can be allocated to the battery and SC in the EMS. From the experimental results, the SC discharge current has been reduced by 46.3% and the charge current by 22.7%. Moreover, the data demonstrates that the proposed algorithm can improve system performance and reduce energy loss. Highlights: An electro-thermal modelling of the HESS is accomplished. The electrical and thermal models of the HESS are represented by equivalent circuit models. Proposed integrated available power for the HESS considering temperature, current, voltage and SOC constraints. State of power combined with energy management strategies. Validation of the energy management strategy is performed under various scenarios proving the effectiveness.