Power flow in heterogeneous battery systems

Bauer, Michaela; Muehlbauer, Markus; Bohlen, Oliver; Danzer, Michael A.; Lygeros, John

doi:10.1016/j.est.2019.100816

Cited by 10 publications

(3 citation statements)

References 27 publications

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“…Additionally, the HP-BS covers power peaks that exceed the power capability of the HE-BS. This approach has been found to be advantageous for BESSs with heterogeneous battery modules, whose differing properties are not to be explicitly exploited [71].…”

Section: ) Calculation Of Hess Power Sharingmentioning

confidence: 99%

Hierarchical Energy Management of Hybrid Battery Storage Systems for PV Capacity Firming and Spot Market Trading Considering Degradation Costs

Wicke,

Bocklisch

2024

IEEE Access

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Section: ) Calculation Of Hess Power Sharingmentioning

confidence: 99%

Hierarchical Energy Management of Hybrid Battery Storage Systems for PV Capacity Firming and Spot Market Trading Considering Degradation Costs

Wicke,

Bocklisch

2024

IEEE Access

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“…The work in [6] proposes a feedback control mechanism to maintain uniform state-ofcharge (SOC) across multiple batteries without considering the cycling cost. The work [7] proposes another feedback mechanism to control power flow in storage systems. A circuit based switching mechanism to operate serially connected battery units is studied in [8].…”

Section: Related Literature and Concluding Remarksmentioning

confidence: 99%

Optimal Cycling of a Heterogenous Battery Bank via Reinforcement Learning

Deulkar¹,

Nair²

2021

Preprint

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We consider the problem of optimal charging/discharging of a bank of heterogenous battery units, driven by stochastic electricity generation and demand processes. The batteries in the battery bank may differ with respect to their capacities, ramp constraints, losses, as well as cycling costs. The goal is to minimize the degradation costs associated with battery cycling in the long run; this is posed formally as a Markov decision process. We propose a linear function approximation based Q-learning algorithm for learning the optimal solution, using a specially designed class of kernel functions that approximate the structure of the value functions associated with the MDP. The proposed algorithm is validated via an extensive case study.

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“…Integrating new batteries in combination with used batteries, that have been only slightly modified to avoid further development costs, is a challenge [4,5]. A battery state dependent load distribution is necessary for the safe operation of a heterogeneous battery system [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Control Concepts for a Decentralized Battery Management System to Optimize Reliability and Battery Operation

Reindl¹,

Eriksson²,

Niemetz³

et al. 2023

Atlantis Highlights in Engineering

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Battery systems are used in a wide range of safety-relevant applications, such as electric vehicles, unmanned aerial vehicles and home storage systems. Safety, reliability and availability of the battery system therefore play a key role. In addition, the useful service lifetime of the batteries determines the environmental impact and economic efficiency of the overall system. One possible solution is to give batteries a second life in applications with lower requirements in terms of dynamic behavior or capacity. Heterogeneous battery systems consist of batteries with differences in cell technology, age, capacity, and optimal operating range. To meet the safety, reliability, and availability requirements a scalable, Decentralized Battery Management System (DBMS) based on a distributed control system is proposed. Batteries, generators, and loads have Local Control Units (LCUs) consisting of a microcontroller, a measurement unit, and a DC/DC converter with adjustable voltage and current limits. These LCUs are the basis for the communication-based, cooperative system control and enhance the reliability and scalability of the battery system compared to conventional centralized structures. They record and manage the operating parameters and provide the basis for predictive energy management and battery residual value estimation. As a fallback strategy, a droop-based control of the DC/DC converters is used in addition to the communication-based one. Transition conditions between the control modes are defined and the control methods are compared and differentiated. The performance and the resulting benefits of batteries are determined by the control strategies. In this paper, the requirements for the control strategies for different operating modes, including startup, severe fluctuations of the DC power line voltage, and safe shutdown, are analyzed. Keywords:Renewable energy sources • battery management system • second life battery • decentralized control • distributed control • control strategies • droop control • battery optimal operation

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Power flow in heterogeneous battery systems

Cited by 10 publications

References 27 publications

Hierarchical Energy Management of Hybrid Battery Storage Systems for PV Capacity Firming and Spot Market Trading Considering Degradation Costs

Hierarchical Energy Management of Hybrid Battery Storage Systems for PV Capacity Firming and Spot Market Trading Considering Degradation Costs

Optimal Cycling of a Heterogenous Battery Bank via Reinforcement Learning

Control Concepts for a Decentralized Battery Management System to Optimize Reliability and Battery Operation

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