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This paper introduces a novel approach to battery management. In contrast to state-of-the-art solutions where a central Battery Management System (BMS) exists, we propose an Embedded Battery Management (EBM) that entirely decentralizes the monitoring and control of the battery pack. For this purpose, each cell of the pack is equipped with a Cell Management Unit (CMU) that monitors and controls local parameters of the respective cell, using its computational and communication resources. This combination of a battery cell and CMU forms the smart cell. Consequently, system-level functions are performed in a distributed fashion by the network of smart cells, applying concepts of self-organization to enable plug-and-play integration. This decentralized distributed architecture might offer significant advantages over centralized BMSs, resulting in higher modularity, easier integration and shorter time to market for battery packs. A development platform has been set up to design and analyze circuits, protocols and algorithms for EBM enabled by smart cells.

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.

With the perennial demand for longer runtime of battery-powered Wireless Sensor Nodes (WSNs), several techniques have been proposed to increase the battery runtime. One such class of techniques exploiting the battery recovery effect phenomenon claims that performing an intermittent discharge instead of a continuous discharge will increase the usable battery capacity. Several works in the areas of embedded systems and wireless sensor networks have assumed the existence of this recovery effect and proposed different power management techniques in the form of power supply architectures (multiple battery setup) and communication protocols (burst mode transmission) in order to exploit it. However, until now, a systematic experimental evaluation of the recovery effect has not been performed with real battery cells, using high-accuracy battery testers to confirm the existence of this recovery phenomenon. In this article, a systematic evaluation procedure is developed to verify the existence of this battery recovery effect. Using our evaluation procedure, we investigated Alkaline, Nickel-Metal Hydride (NiMH), and Lithium-Ion (Li-Ion) battery chemistries, which are commonly used as power supplies for Wireless Sensor Node (WSN) applications. Our experimental results do not show any evidence of the aforementioned recovery effect in these battery chemistries. In particular, our results show a significant deviation from the stochastic battery models, which were used by many power management techniques. Therefore, the existing power management approaches that rely on this recovery effect do not hold in practice. Instead of a battery recovery effect, our experimental results show the existence of the rate capacity effect , which is the reduction of usable battery capacity with higher discharge power, to be the dominant electrochemical phenomenon that should be considered for maximizing the runtime of WSN applications. We outline power management techniques that minimize the rate capacity effect in order to obtain a higher energy output from the battery.