Smartphone users require high Battery Cycle Life (BCL) and high Quality of Experience (QoE) during their usage. These two objectives can be conflicting based on the user preference at run-time. Finding the best trade-off between QoE and BCL requires an intelligent resource management approach that considers and learns user preference at run-time. Current approaches focus on one of these two objectives and neglect the other, limiting their efficiency in meeting users’ needs. In this article, we present UBAR, User- and Battery-aware Resource management, which considers dynamic workload, user preference, and user plug-in/out pattern at run-time to provide a suitable trade-off between BCL and QoE. UBAR personalizes this trade-off by learning the user’s habits and using that to satisfy QoE, while considering battery temperature and State of Charge (SOC) pattern to maximize BCL. The evaluation results show that UBAR achieves 10% to 40% improvement compared to the existing state-of-the-art approaches.

The smart energy grid features real-time monitoring of electricity usage such that it can control the generation and distribution of electricity as well as utilize dynamic pricing in response to the demands. For this purpose, smart metering systems continuously monitor the electricity usage of customers, and report it back to the Utility Provider (UP). This raises privacy concerns regarding the undesired exposure of human activity and time-of-use of home appliances. Photovoltaics (PV) and a residential Electrical Energy Storage (EES) have proven to be effective in mitigating the privacy concerns. However, this comes at several costs: Installation of PV and EES, their subsequent aging and the possibly increased electricity cost. We quantify the trade-off between privacy exposure and financial costs by formulating a stochastic dynamic programming problem. Our analysis shows that i) there is a quantifiable trade-off between the financial cost and privacy leakage, ii) proper control of the system is crucial for both metrics, iii) a strategy solely focusing on privacy results in high financial costs, and iv) that for a typical residential setting, the costs for a trade-off solution lie in the range of 600 US$-1700 US$. As the load flattening has a peak shaving effect desirable for UPs, increasing privacy is mutually beneficial for both, customers and UPs.

Active cell balancing is the process of equalizing the charge levels of individual cells in a series-connected high power Lithium-Ion battery packs to improve its usable capacity. Several hardware circuit architectures for exchanging charge between the cells and multiple heuristics for controlling the balancing architectures have been proposed in the literature. However, formulating an optimal balancing algorithm that guarantees minimum energy dissipation has not been studied so far. In this paper, for the first time, we propose an optimal cell balancing strategy for minimizing the energy dissipation in a charge equalization process. Our proposed optimization approach consists of two stages. In the first stage, we formulate the charge equalization as a Mixed Integer Linear Programming problem for identifying the set of charge transfer pairs of cells that will guarantee minimum energy dissipation. For these obtained pairs, we compute the lower bound for the balancing time considering the constraints of the balancing architecture. In the second stage, we propose an iterative scheduling strategy to achieve this lower bound by solving an Integer Linear Programming problem at each iteration. Multiple case studies show that our proposed strategy results up to 41% less energy dissipation than the state-of-the-art approaches and always achieves the computed lower bound for the balancing time.

Active cell balancing is performed to minimize the variation in the charge levels of the individual cells in a high-power battery pack, to improve its usable capacity. The process of charge equalization is carried out by scheduling pairs of cells to transfer charge over a hardware circuit. Improving the time for charge equalization has been studied in the power electronics and the electronic design automation domains. However, these approaches have focused on the electronics issues and used heuristics to determine the charge transfer schedule. Hence, no optimality results on charge equalization times are known. We, for the first time, take a real-time systems approach and propose an optimal scheduling framework for active cell balancing. The proposed framework employs a hybrid optimization technique consisting of two sequential stages. In the first stage, we solve a mixed-integer linear programming problem to identify the time-optimal set of charge transfers required to achieve charge equalization. In the second stage, we construct a conflict graph based on the obtained charge transfers, to which we apply the minimum vertex coloring algorithm to synthesize the minimum length schedule. Results show that our proposed framework can reduce the charge equalization time by more than 50% (e.g., from 11 h to 5h). Hence, this has real benefits, e.g., in the context of charging electric vehicles. While task and message scheduling problems have been extensively studied in the real-time systems literature, the scheduling problem we study here, has not been addressed before.

Short drive range due to limited battery capacity and high battery depreciation costs persist to be the main deterrents to the wide adoption of Electric Vehicles (EVs). High power battery packs consisting of a large number of battery cells require extensive management, such as State of Charge (SOC) balancing and thermal management, in order to keep the operating conditions within a safe and efficient range. In this paper, we propose a novel State of Health (SOH)-aware active cell balancing technique, which is capable of extending the cycle life of the whole battery pack. In contrast to the state-of-the-art active cell balancing techniques, the proposed technique reduces the load current of cells with low SOH using the active cell balancing architecture. Based on the observation that assigning the smallest possible load current to cells with lower SOH extends cycle life, the technique identifies the most beneficial charge transfers. We find that with our proposed scheme, aging could be mitigated by up to 23.5% over passive cell balancing and 17.6% over active SOC cell balancing.

In this paper, we present Smart2, an advanced smartphone charger that mitigates battery's capacity fading, which until now has usually been ignored. Smart2 exploits the fact that many users charge their phones over night. Since the overnight charging duration is unnecessarily long, the battery is subjected to a high average state of charge (SOC), which accelerates battery aging. Therefore, we delay the charging adaptively to be done shortly before the phone is unplugged. With this scheme, clearly when averaged over the duration of the night, the average SOC is lower and hence aging is reduced. Indicators are a set alarm clock and/or statistics of previous usage. Similarly, we lower the maximum target SOC. To enable this, the main challenges are firstly to find a solution that does not negatively influence the usability and secondly to quantify the achieved savings in terms of aging mitigation. Towards this, we propose a novel charging scheme which can be implemented in the smartphone's firmware. Furthermore, we propose a modified battery charging device that can be used with almost all existing smart phone models. Using our proposed techniques, the average battery cycle life can be nearly doubled from 3.7 to 6.6 years.

Battery-operated portable electronics, from smartphones to notebook computers, are generally sold with a dedicated power supply. The power supply operates the device and also charges the built-in battery. Most users are concerned about the battery aging while the device is operated by the built-in battery. This is the first paper to our knowledge that discovers, analyzes and mitigates the built-in battery aging when the device is operated with the provided power supply. We focus on the fact that in an effort to reduce size and weight, the capacity of the power supply is optimized for the average power demand rather than the maximum power demand. Such a reduced-capacity power supply brings advantages in terms of size, weight and cost but it accelerates the battery aging because the aging progresses even when the device is operated by the power supply, which is different from the expectation of most users. We quantitatively analyze such battery aging with various operating scenarios based on standard benchmark programs. We show that the battery experiences significant aging, i.e., the battery lifetime can be reduced to 23% of its shelf lifetime. Finally, we propose a cost-effective supercapacior hybrid to mitigate such battery aging when the device is operated using the power supply. The simulation results show that 10, 1 and 0.1 mF supercapacitors can reduce the battery aging by 68.6%, 55.1% and 4.6%, respectively.

In this paper, for the first time, we propose a battery- and aging-aware optimization framework for embedded control systems design in electric vehicles (EVs). Performance and reliability of an EV are influenced by feedback control loops implemented into in-vehicle electrical/electronic (E/E) architecture. In this context, we consider the following design aspects of an EV: (i) battery usage, (ii) processor aging of the in-vehicle embedded platform. In this work, we propose a design optimization framework for embedded controllers with gradient-based and stochastic methods taking into account quality of control (QoC), battery usage and processor aging. First, we obtain a Pareto front between QoC and battery usage utilizing the optimization framework. Well-distributed non-dominated solutions are achieved by solving a constrained bi-objective optimization problem. In general, QoC of a control loop highly depends on the sampling period. When the processor ages, on-chip monitors could be used to measure the delay of the critical path, based on which, the processor operating frequency is reduced to ensure correct functioning. As a result, the sampling period gets longer opening up the possibility of QoC deterioration, which is highly undesirable for safety-critical applications in EVs. Utilizing the proposed framework, we take into account the effect of processor aging by re-optimizing the controller design with the prolonged sampling period resulting from processor aging. We illustrate the approach considering electric motor control in EVs. Our experimental results show that the effect of processor aging on QoC deterioration can be mitigated by controller re-optimization with a slight compromise on battery usage.