Cloud-based SOC optimization for predictive energy management and zero emission zone compliance in PHEVs

Abstract

With deteriorating air quality in many cities worldwide failing to meet World Health Organization (WHO) standards, effective countermeasures are urgently needed. In response, cities are implementing zero-emission zones, restricting entry to only zero-emission vehicles like Battery Electric Vehicles and Fuel Cell Electric Vehicles. These measures aim to reduce urban air pollution and improve public health significantly. Despite their ability to operate in pure electric mode under city driving conditions, Plug-in Hybrid Electric Vehicles (PHEVs) are typically prohibited from zero-emission zones due to the potential use of their Internal Combustion Engines, which could compromise air quality improvement efforts. However, advancements in digital maps and Vehicle-to-Everything (V2X) technology present a viable solution to this challenge. Geofencing technology can now be employed to carefully plan and prepare PHEVs’ battery State of Charge (SOC), ensuring that SOC usage is strictly restricted within zero-emission zones.

This study proposes a predictive control strategy for PHEVs, utilizing route information from digital map providers to enable electric driving within zero-emission zones. To achieve this, a supervisory control with Dynamic Programming (DP) is developed in the upper layer to calculate an optimal SOC trajectory considering the zero-emission zone and guide the rule-based controller in the lower level. The high computational effort of DP is addressed by running it in the cloud. In addition, the optimization can be repeated multiple times during driving. The proposed methodology is tested and validated on a demonstrator vehicle in a real-world drive cycle.