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Neighbor discovery (ND) protocols are used for establishing a first contact between multiple wireless devices. The energy consumption and discovery latency of this procedure are determined by the parametrization of the protocol. In most existing protocols, reception and transmission are temporally coupled. Such schemes are referred to as \textit{slotted}, for which the problem of finding optimized parametrizations has been studied thoroughly in the literature. However, slotted approaches are not efficient in applications in which new devices join the network gradually and only the joining devices and a master node need to run the ND protocol simultaneously. For example, this is typically the case in IoT scenarios or Bluetooth Low Energy (BLE) piconets. Here, protocols in which packets are transmitted with periodic intervals (PI) can achieve significantly lower worst-case latencies than slotted ones. For this class of protocols, optimal parameter values remain unknown. To address this, we propose an optimization framework for PI-based BLE-like protocols, which translates any specified duty-cycle (and therefore energy budget) into a set of optimized parameter values. We show that the parametrizations resulting from one variant of our proposed scheme are optimal when one receiver discovers one transmitter, and no other parametrization or ND protocol - neither slotted nor slotless - can guarantee lower discovery latencies for a given duty-cycle in this scenario. Since the resulting protocol utilizes the channel more aggressively than other ND protocols, beacons will collide more frequently. Hence, due to collisions, the rate of successful discoveries gracefully decreases for larger numbers of devices discovering each other simultaneously. We also propose a scheme for configuring the BLE protocol (and not just BLE-\textit{like} protocols)., Comment: To appear in the IEEE Transactions on Mobile Computing (TMC) 2020

Active cell balancing is the process of equalizing the State-of-Charge (SoC) of all cells in a series-connected battery pack via charge transfers. While several balancing architectures and equalization strategies exist in the literature, optimization of the circuit components in a balancing architecture has not been sufficiently studied so far. Intuitively, one would select components with low parasitic resistances to minimize the energy dissipation. However, as will be shown in this paper, this might not be an optimal choice in most situations. In this paper, we propose an optimization framework that performs an efficient design space exploration to identify the energy-efficient circuit configuration of a balancing architecture. Moreover, for the first time, we propose a methodology to obtain the optimal operating frequency of an active cell balancing architecture and a control circuit to maintain the operation at the optimal frequency. For this purpose, we derive a closed-form analytical model of the charge transfer process with which we obtain the performance metrics required for our framework. Case studies show that such optimally dimensioned active cell balancing architectures operated at the optimal frequency reduce the energy dissipation by approximately 49% compared to the intuitive approach of selecting components with least parasitic resistances.

Active cell balancing improves the performance of a battery pack by transferring charge from one cell to another. Associated design questions require multiple simulations with 100 cells over several hours. Since the most efficient transfer methods switch between phases in the kilohertz range, these simulations require high computational effort or reduced accuracy. To enable detailed analysis on a large scale, this paper includes state-of-the-art electrical battery models in active balancing simulation while keeping the computation effort for one transfer in the low millisecond range. This is achieved in three steps. First, we model the dynamics of each transfer phase using standard equivalent circuit abstraction. Next, we find closed form equations for the so-defined phase dynamics, yielding an iterative approach that saves computation time by replacing the numerical solver. Finally, we employ error control techniques to aggregate phases in that iteration, systematically reducing the millions of phase evaluations that would be necessary otherwise. Our experiments show that the speedup from equivalent circuit dynamics to error-controlled aggregation almost reaches five orders of magnitude while introducing virtually no additional error. This enables simulations of realistic balancing scenarios in less than a second and is hence suitable for design space exploration.

High-voltage battery packs consist of series-connected lithium-ion cells and require sophisticated battery management systems (BMSs) to maintain safe operating conditions. Active cell balancing is an important task of a BMS, performed in order to improve the usable capacity of the battery pack by equalizing the charge levels of individual cells. With the emerging trend of distributed BMS topologies, the associated balancing architectures are required to be modular, consisting of homogeneous units that minimize integration efforts. In this paper, we propose a modular active charge balancing architecture along with its control scheme for implementation toward such distributed BMSs. Compared with existing approaches, our proposed architecture provides increased charge transfer capabilities, with reduced hardware and control complexity. We propose a closed-form analytical model of the balancing architecture, which can be used to perform fast system-level simulation studies and design space exploration for analyzing efficient device combinations. A hardware implementation of the proposed balancing architecture is developed and measurements made with it are used to validate each part of our analytical model. Using the validated analytical model, we performed a case study, which shows that our proposed architecture provides a 14.5 % improvement in charge transfer efficiency compared with existing approaches.

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.

Electrical energy storage (EES) systems, specifically in the form of high power lithium-ion (Li-Ion) battery packs, are gaining more importance mainly due to the increased penetration of renewable energy sources and the rapid proliferation of electric vehicle (EV) technologies. Battery management is a critical functionality in an EES, which maintains safe operation and also improves the efficiency of the system. With the increasing number of applications using battery packs and the demand for shorter time to market, the system integration aspects are gaining more attention. This has resulted in a natural trend towards decentralization of the battery management system (BMS) where the sensing, computation, and control units are distributed close to each cell, providing a high degree of scalability and customization-free plug and play integration. However, there exist several open challenges in the development of such distributed systems that requires special set of design automation tools to be developed. One of the major requirements for accelerating the design of such decentralized BMSs is prototyping where custom hardware and software implementations are required to be developed increasing the overall design time and cost. To overcome this, in this chapter, we present a modular hardware/software development platform for decentralized BMSs that can be used for evaluating the functionality of different BMS circuit architectures, active cell balancing topologies, and decentralized battery management algorithms. All design files of our proposed development platform are made publicly accessible and can be easily reproduced thereby saving the effort and time required for developing custom prototyping platforms.

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.

Recently, reconfigurable battery packs that can dynamically modify the electrical connection topology of their individual cells are gaining importance. While several circuit architectures and management algorithms are proposed in the literature, the electrical characteristics of the reconfiguration circuit architectures are not sufficiently studied so far. In this paper, we derive a detailed analytical model for a state-of-the-art reconfiguration architecture capturing the losses introduced by the parasitic resistances of the circuit components. Based on this model we evaluate a novel fast charging strategy, which for the first time utilizes the reconfigurable battery pack architecture in order to reduce the losses during fast charging by switching between parallel and series connection of the cells. In order to do this, we consider the electrical characteristics of the reconfigurable circuit components. Since the component resistances are the main driver for the power losses during charging, we are able to give a realistic perspective on the overall pack performance and the efficiency increase due to the utilized fast charging strategy. Furthermore, using the analytical model, we highlight the challenges faced by existing reconfiguration architectures using state-of-the-art components and we derive specifications for the switches for further improving the energy efficiency. Experimental results show that our strategy improves the charging efficiency by up to 10 %.

This paper presents an overview of recent trends in Battery System Management Architectures (BSMAs). After introducing the main characteristics of large battery packs, the state of the art in BSMAs is discussed. Two emerging concepts are in the focus of this contribution. On the one hand, there is a development from centralized battery management architectures with a single control entity towards decentralized management where the computational resources are distributed across the battery pack and, hence, move closer to the individual battery cells. This enables a more scalable and modular battery system architecture, while, at the same time, posing challenges regarding hardware and management algorithm design. On the other hand, the static setup of the series- and parallel-connected cells forming the battery pack may be developed towards a reconfigurable architecture such that the electrical topology of the pack can be adaptively changed. Such reconfigurability could increase the reliability of battery packs and reduce management efforts such as cell balancing. At the same time, limited energy efficiency of the additional hardware poses a challenge. We give an outlook how these two trends could be combined into distributed reconfigurable BSMAs. This introduces a set of challenges which have to be solved in order to benefit from the increased scalability, reliability and safety such designs could offer.

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.

Large battery packs consisting of a high number of cells are essential in electric vehicles as well as in smart grids as stationary energy buffers. In this context, active cell balancing techniques improve the lifetime and capacity of battery packs significantly by equalizing charge at runtime. Modern balancing circuits rely on switching schemes to transfer charge between cells via energy storage elements such as inductors or capacitors. Verifying correct functionality of complex architectures can become a non-trivial task where circuit and control have to be considered concurrently. For this purpose, we provide a framework for the verification of balancing architectures, using a methodology that takes advantage of graph search algorithms. While this paper focuses on inductor-based architectures, the proposed approach might also be extended to other storage elements such as capacitors or transformers. The experimental results based on several case studies give evidence that a manual verification becomes impractical and our framework is capable of either proving correctness or delivering a counter-example.