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Abstract
With the increasing application of battery energy storage in buildings, networks and transportation, an emerging challenge to overall system resilience is in understanding the constituent asset health. Current battery energy storage considerations focus on adhering to the technical specification of the service in the short term, rather than the long-term consequences to battery health. However, accurately determining battery health generally requires invasive measurements or computationally expensive physics-based models which do not scale up to a fleet of assets cost-effectively. This paper alternatively proposes capturing cumulative maloperation through a physics model-free proxy for cell health, articulated via the strong influence misuse has on the internal chemical state. A Hidden Markov Chain approach is used to automatically recognize violations of chemistry specific usage preferences from sequences of observed charging actions. The resulting methodology is demonstrated on distribution network level electrical demand and generation data, accurately predicting maloperation under a number of battery technology scenarios.
Highlights
Electrical Energy Storage (EES) is becoming an increasingly important component in power distribution networks, with its ability to accommodate uncertain demand and intermittent renewable generation reducing the need for excessive standby generation [1], [2], accommodating peak demands, grid frequency regulation [2] and other services
As battery storage aggregation becomes more commonplace, the management of the constituent battery assets will require health metrics to ensure ongoing contractual turn up and turn down commitments are fulfilled to their agreed capacity
This paper has presented a non-invasive approach that predicts the extent to which a battery asset has been maloperated
Summary
Electrical Energy Storage (EES) is becoming an increasingly important component in power distribution networks, with its ability to accommodate uncertain demand and intermittent renewable generation reducing the need for excessive standby generation [1], [2], accommodating peak demands, grid frequency regulation [2] and other services. Stephen: Model-Free Non-Invasive Health Assessment for BES Assets the electrochemical battery model [11]–[13], being the first principle in nature, is one of the most accurate models and the least suitable to EES applications due to its complexity, computational effort and need for detailed parameter values These models will capture battery nonlinear behaviour but will not directly address the estimation of the state of charge (SoC) or state of health (SoH) [12], [32]. It is difficult to reproduce realistic operating regimes in the laboratory settings, the resulting model can be more error prone in online prediction [36] Another group of data-driven models are stochastic models of battery discharging and charging processes based on Markov Chains that were developed to model primary (non-rechargeable) [29], [30] and secondary (rechargeable) batteries [23], [31].
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