Exergy and exergoeconomic analyses and optimization of thermal management systems in electric and hybrid electric vehicles

Abstract

With the recent improvements in battery technologies, in terms of energy density, cost and size, the electric (EV) and hybrid electric vehicle (HEV) technologies have shown that they can compete with conventional vehicles in many areas. Although EVs and HEVs offer potential solutions for many key issues related to conventional vehicles, they still face considerable challenges that prevent the widespread commercialization of these technologies, such as thermal management of batteries and electrification. In this PhD thesis, a liquid thermal management system (TMS) for hybrid electric vehicles is investigated and evaluated against alternative thermal management systems, and optimal parameters are selected to maximize the system efficiency. In order to achieve this goal, a model of the liquid thermal management system is established to determine the irreversibilities and second-law efficiencies associated with the overall system and its components. Furthermore, the effects of different configurations, refrigerants and operating conditions are analyzed with respect to conventional exergy analyses. In addition, advanced exergy analyses are also conducted in order to better identify critical relationships between the TMS components and determine where the system improvement efforts should be concentrated. Moreover, investment costs are calculated and cost formation of the system is developed in order to evaluate the TMS with respect to exergoeconomic principles and provide corresponding recommendations. Environmental impact correlations are developed, along with a cradle-to-grave life cycle assessment (LCA), to highlight components causing significant environmental impact, and to suggest trends and possibilities for improvement based on the exergoenvironmental variables. Finally, the TMS is optimized using multi-objective evolutionary algorithm which considers exergetic and exergoeconomic as well as exergetic and exergoenvironmental objectives simultaneously with respect to the decision variables and constraints. Based on the conducted research for the studied system under the baseline conditions, the exergy efficiency, total cost rate and environmental impact rate are determined to be 0.29, ¢28/h and 77.3 mPts/h, respectively. The exergy destruction associated with each component is split into endogenous/exogenous and avoidable/unavoidable parts, where the exogenous exergy destruction is determined to be relatively small but significant portion of the total exergy destruction in each component (up to 40%), indicating a moderate level of interdependencies among the components of the TMS. Furthermore, it is determined that up to 70% of the exergy destruction calculated within the components could potentially be avoided. According to the analyses, electric battery is determined to have the highest exergoeconomic and exergoenvironmental importance in the system, with cost rate of ¢3.5/h and environmental impact value of 37.72 mPts/h, due to the high production cost of lithium ion batteries and the use of copper and gold in the battery pack. From an exergoeconomic viewpoint, it is determined that the investment costs of the condenser and evaporator should be reduced to improve the costeffectiveness of the system. On the other hand, from an exergoenvironmental viewpoint, all the component efficiencies (except for the battery) should be improved in order to reduce the total environmental impact even if it increases the environmental impact during production of the components. In addition, it is determined that the coolant pump and the thermal expansion valve before the chiller are relatively insignificant from exergoeconomic and exergoenvironmental perspectives. Subsequently, objective functions are defined and decision variables are selected, along with their respective system constraints, in order to conduct single and multiple objective optimizations for the system. Based on the single objective optimizations, it is determined that the exergy efficiency could be increased by up to 27% using exergy-based optimization, the cost can be reduced by up to 10% using cost-based optimization and the environmental impact can be reduced by up to 19% using environmental impact-based optimization, at the expense of the nonoptimized objectives. Moreover, multi-objective optimizations are conducted in order to provide the respective Pareto optimal curve for the system and to identify the necessary trade-offs within the optimized objectives. Based on the exergoeconomic optimization, it is concluded that 14% higher exergy efficiency and 5% lower cost can be achieved, compared to baseline parameters at an expense of 14% increase in the environmental impact. Furthermore, based on the exergoenvironmental optimization, 13% higher exergy efficiency and 5% lower environmental impact can be achieved at the expense of 27% increase in the total cost.