| dc.description.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. | en |
