Battery Life Management Research Articles

Degradation Evaluation of Lithium-Ion Batteries in Plug-In Hybrid Electric Vehicles: An Empirical Calibration

Battery life management is critical for plug-in hybrid electric vehicles (PHEVs) to prevent dangerous situations such as overcharging and over-discharging, which could cause thermal runaway. PHEVs have more complex operating conditions than EVs due to their dual energy sources. Therefore, the SOH estimation for PHEV vehicles needs to consider the specific operating characteristics of the PHEV and make calibrations accordingly. Firstly, we estimated the initial SOH by combining data-driven and empirical models. The data-driven method used was the incremental state of charge (SOC)-capacity method, and the empirical model was the Arrhenius model. This method can obtain the battery degradation trend and predict the SOH well in realistic applications. Then, according to the multiple characteristics of PHEV, we conducted a correlation analysis and selected the UF as the calibration factor because the UF has the highest correlation with SOH. Finally, we calibrated the parameters of the Arrhenius model using the UF in a fuzzy logic way, so that the calibrated fitting degradation trends could be closer to the true SOH. The proposed calibration method was verified by a PHEV dataset that included 11 vehicles. The experiment results show that the root mean square error (RMSE) of the SOH fitting after UF calibration can be decreased by 0.2–14% and that the coefficient of determination (R2) for the calibrated fitting trends can be improved by 0.5–32%. This provides more reliable guidance for the safe management and operation of PHEV batteries.

Guest Editorial: Fast, Superfast, and Ultra-Superfast Intelligent and Smart Charging Solutions for Electric Vehicles

To Promote and meet the projected levels of electric vehicle (EV) penetration, the development of charging infrastructure, i.e., both slow charging and dc-fast charging, is essential. Slow/onboard chargers pose weight, efficiency, and thermal management challenges to the designers, while the off-board fast/superfast/ultra-superfast chargers pose efficiency, power rating, and control challenges. In particular, the installation of dc fast chargers (including fast/superfast/ultra-superfast) is also dictated by the impact on the utility grid and the existing loads. New installation or retrofitting of the existing infrastructure is another significant investment challenge jointly explored by stakeholders, utility companies, and car/charger manufacturers. Another primary concern is battery life management limiting the charging current intake by the EV battery.

Lightweight coordination model for mobile collaborative mapping

The rapid development of mobile devices features offers new opportunities for mobile peer-to-peer computing that can be used for providing location-based services and social/collaborative platforms using short-range networks. We are interested in collaborative mappings which provide computer support for nomadic users to manipulate at will and simultaneously a shared map on mobile devices. However, maintaining data consistency in mobile environment remains a challenging issue due to the arbitrary order of updates reception and the battery-life management. We propose a low-cost and flexible mobile collaborative mapping using simple primitive updates and easily customisable for rich features-based maps. No central coordination is necessary for preserving the shared map consistency. Indeed, we design a concurrency control protocol based on operational transformation (OT) approach to synchronise collaborative works in fully decentralised way. With a straightforward deployment on P2P networks, our application supports dynamic groups where mobile users can participate in and quit freely.

Toward Eco-Friendly Smart Mobile Devices for Smart Cities

It is expected that the incessant use of SBSs by indoor mobile users to achieve high data rate and high energy efficiency for their smart mobile devices will drive ultra dense deployment of SCNs within MCNs, specifically in future smart cities. However, SCN densification has a hard effect on the deterioration of the energy efficiency and QoS throughput of mobile users equipped with smart mobile devices like smartphones, laptops, PDAs, and tablets. This degradation is due to the severe cross-tier interferences occurring in ultra dense co-channel deployments of SCNs within MCNs. The present article intends to study the energy efficiency of mobile users for better battery life management of their smart mobile devices through minimal energy consumption during their uplink transmissions. The proposed solution, based on a fully decentralized algorithm for sharing time access executed by SBSs and multi-homing capabilities of macrocellular users, improves the energy efficiency of mobile users for better battery life, and keeps their QoS throughput requirements. Therefore, it leads to the eco-friendly smart mobile devices required in future smart cities. The results validate our proposed solution and show the improvement achieved compared to the conventional access control mechanisms. Furthermore, our scheme requires no coordination among SBSs and macro base stations, which reduces the huge signaling overhead when using cooperative schemes.

Optimal charging for general equivalent electrical battery model, and battery life management

The contributions of this paper are threefold. First, we present the linear quadratic solution to optimally charge a Li-ion battery in a general form. Although the battery model considered here is a circuit comprised of an open-circuit voltage (OCV), a series resistance, and an RC circuit, the methodology is applicable to any electrical circuit model of a battery. A combination of different cost functions is considered including: time-to-charge, energy loss, and temperature rise index. We discuss the effect of different weightings in the cost functions on the current and voltage profiles. Second, we present two models for normalized battery capacity as a function of the number of cycles and two charge control parameters, viz., maximum terminal voltage of the battery and maximum charge current. These models are compared to a bi-exponential capacity model. The effectiveness of the proposed models for forecasting the battery capacity is validated using the experimental data. Third, these models are used for battery life management by developing an optimal charging parameter selection method which provides the best setpoint values for the control variables to achieve a pre-specified desired “useful cycle life” while attaining the fastest possible time-to-charge. The proposed optimal charging parameter selection method is illustrated via numerical results.