Electric vehicles are set to be the dominant form of transportation in the near future and Lithium-based rechargeable battery packs have been widely adopted in them. Battery packs need to be constantly monitored and managed in order to maintain the safety, efficiency and reliability of the overall electric vehicle system. A battery management system consists of a battery fuel gauge, optimal charging algorithm, and cell/thermal balancing circuitry. It uses three non-invasive measurements from the battery, voltage, current and temperature, in order to estimate crucial states and parameters of the battery system, such as battery impedance, battery capacity, state of charge, state of health, power fade, and remaining useful life. These estimates are important for the proper functioning of optimal charging algorithms, charge and thermal balancing strategies, and battery safety mechanisms. Approach to robust battery management consists of accurate characterization, robust estimation of battery states and parameters, and optimal battery control strategies. This paper describes some recent approaches developed by the authors towards developing a robust battery management system.

In this paper, the first of a series of papers on optimal battery charging, we present a closed-form solution to the problem of optimally charging a Li-ion battery. A combination of three cost functions is considered as the objective function: time-to-charge (TTC), energy losses (EL), and a temperature rise index (TRI). First, we consider the cost function of the optimization problem as a weighted sum of TTC and EL. We show that the optimal charging strategy in this case is the well-known Constant Current-Constant Voltage (CC-CV) policy with the value of the current in the CC stage being a function of the ratio of weighting on TTC and EL and of the resistance of the battery. Then, we extend the cost function to a weighted sum of TTC, EL and TRI and derive an analytical solution for the problem. It is shown that the analytical solution can be approximated by a CC-CV with the value of current in the CC stage being a function of ratio of weighting on TTC and EL, resistance of the battery and the effective thermal resistance. Index Terms battery charging, optimal charging, time to charge, open circuit voltage, state of charge.

This paper presents a mixed-initiative tool for multiobjective planning and asset routing (TMPLAR) in dynamic and uncertain environments. TMPLAR is built upon multiobjective dynamic programming algorithms to route assets in a timely fashion, while considering fuel efficiency, voyage time, distance, and adherence to real world constraints (asset vehicle limits, navigator-specified deadlines, etc.). TMPLAR has the potential to be applied in a variety of contexts, including ship, helicopter, or unmanned aerial vehicle routing. The tool provides recommended schedules, consisting of waypoints, associated arrival and departure times, asset speed and bearing, that are optimized with respect to several objectives. The ship navigation is exacerbated by the need to address multiple conflicting objectives, spatial and temporal uncertainty associated with the weather, multiple constraints on asset operation, and the added capability of waiting at a waypoint with the intent to avoid bad weather, conduct opportunistic training drills, or both. The key algorithmic contribution is a multiobjective shortest path algorithm for networks with stochastic nonconvex edge costs and the following problem features: 1) time windows on nodes; 2) ability to choose vessel speed to next node subject to (minimum and/or maximum) speed constraints; 3) ability to select the power plant configuration at each node; and 4) ability to wait at a node. The algorithm is demonstrated on six real world routing scenarios by comparing its performance against an existing operational routing algorithm.

A battery fuel gauge (BFG) helps to extend battery life by tracking the state of charge (SOC) and many other diagnostic features. In this paper, we present an approach to validate the SOC and time-to-shutdown (TTS) estimates of a BFG. Hardware-in-the-loop (HIL) testing under realistic usage scenarios provides a means for BFG algorithm evaluation and provides insights into practical implementation and testing of BFG algorithms in battery management systems. We report the details of a HIL system that was designed to validate the SOC and TTS estimation capability of BFG algorithms; different current load profiles were synthesized to replicate typical battery usage in portable electronic applications; the HIL system is automated with the help of programmable current profiles and is designed to operate at various controlled temperatures; three performance validation metrics are formulated for an objective assessment of SOC and TTS tracking algorithms. The HIL setup and the performance validation metrics are used to evaluate a BFG developed by the authors using three different batteries at temperatures ranging from −20 • C to 40 • C. Index Terms Battery management system (BMS), battery fuel gauge (BFG), state of charge (SOC) tracking, hardware-in-the-loop (HIL) validation.

In this paper, we consider the problem of state-of-charge estimation for rechargeable batteries. Coulomb counting is a well-known method for estimating the state of charge, and it is regarded as accurate as long as the battery capacity and the beginning state of charge are known. The Coulomb counting approach, on the other hand, is prone to inaccuracies from a variety of sources, and the magnitude of these errors has not been explored in the literature. We formally construct and quantify the state-of-charge estimate error during Coulomb counting due to four types of error sources: (1) current measurement error; (2) current integration approximation error; (3) battery capacity uncertainty; and (4) timing oscillator error/drift. It is demonstrated that the state-of-charge error produced can be either time-cumulative or state-of-charge-proportional. Time-cumulative errors accumulate over time and have the potential to render the state-of-charge estimation utterly invalid in the long term.The proportional errors of the state of charge rise with the accumulated state of charge and reach their worst value within one charge/discharge cycle. The study presents methods for reducing time-cumulative and state-of-charge-proportional mistakes through simulation analysis.

A battery management system (BMS) plays a crucial role to ensure the safety, efficiency, and reliability of a rechargeable Li-ion battery pack. State of charge (SOC) estimation is an important operation within a BMS. Estimated SOC is required in several BMS operations, such as remaining power and mileage estimation, battery capacity estimation, charge termination, and cell balancing. The open-circuit voltage (OCV) look-up-based SOC estimation approach is widely used in battery management systems. For OCV lookup, the OCV–SOC characteristic is empirically measured and parameterized a priori. The literature shows numerous OCV–SOC models and approaches to characterize them and use them in SOC estimation. However, the selection of an OCV–SOC model must consider several factors: (i) Modeling errors due to approximations, age/temperature effects, and cell-to-cell variations; (ii) Likelihood and severity of errors when the OCV–SOC parameters are rounded; (iii) Computing system requirements to store and process OCV parameters; and (iv) The required computational complexity of real-time OCV lookup algorithms. This paper presents a review of existing OCV–SOC models and proposes a systematic approach to select a suitable OCV–SOC for implementation based on various constraints faced by a BMS designer in practical application.

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