Degradation-conscious charge management: Comparison of different techniques to include battery degradation in Electric Vehicle Charging Optimization
Tomás Montes, Ferran Pinsach Batet, Lucía Igualada, Joshua Eichman
Abstract
Electric Vehicles (EVs) growth has brought opportunities to reduce carbon emissions and dependence on fossil fuel; however, vehicle charging must be managed to maximize the benefit to users and society. Managed charging typically focuses on reducing energy costs; however, this study shows the importance of considering degradation as part of charge management. To do so, we propose five EV energy management models from the point of view of the Charging Station Operator. The models include three different methods to consider battery degradation: 1) implementing good usage practices, 2) direct evaluation of a semi-empirical battery degradation model, and 3) introducing economic weighting for charge rate and State of Charge (SOC) degradation into the objective function. While the need to include degradation is clearer for bidirectional charging, this paper argues that there is also a strong incentive to include degradation-conscious managed charging for unidirectional charging. More than three hundred scenarios have been tested considering different State of Health (SOH) conditions, SOC, maximum available charging power, parking time, and energy price tariff. For the defined scenarios, transitioning from immediate charging to any level of smart charging reduces the total operation costs – considering both energy and degradation costs – by between 13.4 % and 14.6 %. Compared to basic smart charging, implementing degradation-conscious smart charging models could further reduce operation costs by 0.88 % to 1.39 %. This indicates that integrating battery degradation considerations, while not as important as energy prices, still plays an important role in reducing the total cost of charging and reducing environmental impact by extending the EV's battery life. This paper also characterizes the strengths and weaknesses of each model, suggesting areas for improvement and ways to encourage the adoption of the proposed methods.