Method of Estimating Electric Vehicle Charge Time Based on Neural Networks Stellantis

Format:

Book

Conference/Event

Author/Creator:

Xie, Zhentao, author.

Shojaei, Sina, author.

Weslati, Feisel, author.

Conference Name:

WCX SAE World Congress Experience (2026-04-14 : Detroit, Michigan, United States)

Language:

English

Subjects (All):

Vehicle charging.

Charging stations.

Thermal management.

Neural networks.

Control systems.

Architecture.

Local Subjects:

Vehicle charging.

Charging stations.

Thermal management.

Neural networks.

Control systems.

Architecture.

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2026

Summary:

Accurate prediction of electric vehicle charging time is critically hindered by dynamic, non-linear factors including battery aging which is indicated by the State of Health (SOH), substantial power diversion to thermal management systems in extreme temperatures, fluctuating user-defined accessory loads, and hardware limitations of the charging infrastructure. Traditional estimation methods, reliant on static models or predefined calibrations, fail to adapt to these real-world variables, leading to inaccurate predictions and user dissatisfaction. This paper presents a novel data-driven estimation framework utilizing a tailored feedforward neural network architecture specifically designed for this complex task. The model processes a sensitive set of inputsincluding initial State of Charge (SOC), SOH, battery temperature, charging station power level and user-selected target SOCto effectively capture the intricate, non-linear interdependencies governing the charging process. The network is trained offline using the Levenberg-Marquardt algorithm, which optimizes network complexity and mitigates overfitting, ensuring robust generalization without reliance on explicit electrochemical equations. A cornerstone of this invention is its continuous offline learning and update strategy; new field data from diverse charging scenarios is aggregated to periodically retrain and rigorously validate improved network parameters. These updated models are deployed seamlessly to vehicles via Flash-Over-The-Air updates, enabling the system to adapt to battery degradation and evolving usage patterns throughout the vehicle's lifespan. Validation under a wide range of conditions demonstrates a substantial increase in prediction accuracy compared to conventional model-based and calibration-based approaches. This solution, engineered for real-time deployment in vehicle control units, significantly enhances charging transparency, reliability, and overall user satisfaction by providing consistently accurate remaining charge time estimates

Notes:

Vendor supplied data

Access Restriction:

Restricted for use by site license