Determining the required heater power for battery thermal management system in an electric mini-loader under winter conditions using operational data

BACKGROUND: The relevance of the study is driven by the rapid development of the market for electric special-purpose vehicles, designed for utility services tasks in particular. In the context of tightening requirements for energy efficiency and performance, a key challenge is optimizing the performance of the most energy-intensive systems. The scientific problem lies in the lack of representative data on the performance of the traction battery thermal management system under real-world operating conditions characterized by high-amplitude loads caused by hydraulic equipment. The first step in solving this problem is to obtain the aforementioned data using telematics monitoring methods for subsequent analysis and identification of objective patterns.

AIM: Identification of quantitative relationships between the load profiles of an electric compact loader and the operating parameters of its traction battery thermal management system under real-world operating conditions.

METHODS: The study employed the method of telematics operational data logging. Field experiments involved operation of the loader in typical scenarios with hydraulic equipment, such as a road brush. Data on the power consumption of the traction electric motors and the hydraulic system electric motor, temperature sensors of the battery thermal management system, heater power, and battery current were collected using telematics.

RESULTS: Data on the real-world operation of the compact loader in winter conditions, obtained through telematics data logging, are presented. Relationships and patterns were established (e.g., the correlation between load current and heating power, the influence of ambient temperature). The obtained results reveal the actual behavior of the thermal management system during real-world winter operation.

CONCLUSION: The acquired data will make upcoming optimization of the thermal management system’s control algorithms possible, leading to reduced energy consumption. The results form the basis for developing a predictive system that reduces downtime and maintenance costs compared to diagnostics performed after a fault occurs.