Predictive optimization method for the waste heat recovery strategy in an electric vehicle heat pump system

Abstract

To supplement the insufficient heating capacity of heat pump system in winter, electric vehicles (EVs) utilizes the waste heat from electric devices in cabin heating. Conventional waste heat recovery strategy (WHRS) simply recovers heat at certain temperature level, even though the optimal temperature level changes depending on the operating conditions. Here, we suggest a predictive optimization method, which derives the optimal temperature level of WHRS based on the model prediction. We subdivided the temperature levels of WHRSs into three: conventional (low), multi-level (intermediate), and direct (high). To evaluate the performance of WHRS at each temperature level, a transient heat pump and electric device model was established based on the experimental data. We investigated the effect of driving speed, duration, and ambient temperature on the performance of WHRSs, and demonstrated that the optimal WHRS changes under different operating conditions. As the model estimates the dynamic performances of WHRSs with given driving conditions, the optimal WHRS can be derived prior to the actual driving. By utilizing the optimal WHRS suggested by model prediction method, power consumptions of EV were saved up to 13 % compared with conventional WHRS by recovering the waste heat at the optimal temperature level.