ENERGY-EFFICIENT COMBINED MICROCLIMATE SYSTEM FOR AUTOMOTIVE REPAIR FACILITIES

Abstract

The paper addresses the problem of energy-efficient microclimate provision in automotive repair facilities, which are characterised by significant year-round thermal demand for heating, ventilation, and domestic hot water due to elevated air-exchange rates required to remove vehicle exhaust gases, welding fumes, and solvent vapours. Conventional gas-fired heating systems result in high primary energy consumption and substantial CO₂ emissions, motivating the search for renewable-based alternatives. A combined SAHP (Solar-Assisted Heat Pump) system is proposed, integrating an air-to-water heat pump, a flat-plate solar collector array, and a stratified thermal energy storage tank. An extended mathematical model is developed that includes: the Hottel–Whillier–Bliss equation with the incidence angle modifier IAM accounting for diurnal and seasonal variations of beam radiation; an exergy analysis of the heat pump cycle determining irreversibility losses E_x,loss in the compressor, condenser, expansion valve, and evaporator; a multi-node (N = 4) stratified tank model that captures vertical temperature distribution and inter-node convective–conductive heat transfer; a recuperative heat exchanger model based on the ε–NTU method; and a seasonal performance factor (SPF) optimisation criterion evaluated over a typical meteorological year for the Vinnytsia region. The optimisation task determines the optimal collector area A_sol* and tank volume V_tank* that maximise SPF subject to capital cost constraints, formulated as a constrained nonlinear programming problem and solved using a hybrid genetic algorithm. Results show that accounting for tank stratification improves SPF prediction accuracy by 12–18% compared to a conventional single-zone fully-mixed model, which systematically overestimates useful storage capacity. Integration of solar thermal energy raises the system COP by 30–77% relative to a standalone ASHP across the heating season, while the expected SPF = 3.8–4.3 corresponds to a primary energy factor E_PE = 0.47–0.53 — 1.7–2.1 times lower than a comparable gas boiler. IoT-based model predictive control (MPC) leveraging short-term solar irradiance and load forecasts is discussed for operational optimisation, demonstrating an additional 6–9% reduction in seasonal electricity consumption. The results are applicable to the design and retrofit of heat supply systems for industrial automotive enterprises, service stations, and similar facilities pursuing decarbonisation and reduced operating costs.