This paper investigates the real-time energy optimization of battery powered vapor compression systems (VCS) for commercial vehicles. Battery powered VCS are critical for maintaining passenger comfort in engine-off situations, such as for long-haul truck drivers who sleep inside their vehicle overnight. This paper proposes the use of extremum seeking control (ESC), a class of real-time, model-free optimization algorithms, to determine the combination of system inputs that minimizes VCS power consumption while meeting desired cooling requirements. We examine the implementation of three different ESC algorithms established in literature: perturbation-ESC (P-ESC), least squares-ESC (LS-ESC), and recursive least squares-ESC (RLS-ESC) on an experimental setup consisting of a battery powered VCS cooling a vehicle cabin. Experimental results demonstrate significant increases (up to 33%) in energy efficiency and battery life through algorithm use, with RLS-ESC being the most effective of the three.

本文研究了商用车辆电池供电的蒸气压缩系统（VCS）的实时能量优化。电池供电的VCS对于在发动机熄火的情况下保持乘客的舒适度至关重要，例如对于过夜睡在车内的长途卡车司机。本文提出了使用极值搜索控制（ESC）（一类实时，无模型的优化算法）来确定系统输入的组合，以最大程度地降低VCS功耗，同时满足所需的散热要求。我们研究了在文献中建立的三种不同的ESC算法的实现：扰动ESC（P-ESC），最小二乘ESC（LS-ESC）和递归最小二乘ESC（RLS-ESC）。电池供电的VCS冷却车厢。