Work

Optimization of thermal preconditioning in BEVs performing daily driving cycles

Year: 2026

Type: article

Abstract: The electrification of the automotive sector is a pivotal strategy for global decarbonization; however, the widespread adoption of Battery Electric Vehicles (BEVs) faces a significant technical barrier: operational robustness in cold weather climates. This paper presents a computationally efficient optimization framework for the thermal preconditioning of BEVs performing daily driving cycles. The proposed approach utilizes a Control-Oriented Model (COM) embedded in an optimization algorithm to determine the pre-trip battery and cabin conditioning that minimizes the total grid energy of a predicted daily cycle with different segments, which can be classified into three main classes: parking, charging, and driving. The validation against a detailed dynamic model confirms the robustness of the approach, with energy prediction errors consistently below 0.4%. Meanwhile, the algorithm results in energy reductions of 1% to 9.6%, depending on the ambient temperature and route. The analysis highlights that the optimal strategy is highly sensitive to the interplay between ambient temperature and route characteristics including speed profile, topography and charger availability.

Source: Applied Thermal Engineering

DOI: 10.1016/j.applthermaleng.2026.133505

Authors: Ignacio Laengle, Benjamín Pla, Pau Bares, André Nakaema Aronis, Richard Cottet, Quyen-Anh Nguyen

Institutions: Universitat Politècnica de València, Valeo (France)

Language: English

Topic: Advanced Battery Technologies Research

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure, Climate action, Sustainable cities and communities

Open Access status: hybrid

Estimated APC paid: $3,660