Battery Degradation Maps for Power System Optimization and as a Benchmark Reference

This paper presents a novel method to describe battery degradation. We use the concept of degradation maps to model the incremental charge capacity loss as a function of discrete battery control actions and state of charge. The maps can be scaled to represent any battery system in size and power. Their convex piece-wise affine representations allow for tractable optimal control formulations and can be used in power system simulations to incorporate battery degradation. The map parameters for different battery technologies are published making them an useful basis to benchmark different battery technologies in case studies

Al/Cl2 molten salt battery

Molten salt battery has been developed with theoretical energy density of 5.2 j/kg (650 W-h/lb). Battery, which operates at 150 C, can be used in primary mode or as rechargeable battery. Battery has aluminum anode and chlorine cathode. Electrolyte is mixture of AlCl3, NaCl, and some alkali metal halide such as KCl

Factoring the Cycle Aging Cost of Batteries Participating in Electricity Markets

When participating in electricity markets, owners of battery energy storage systems must bid in such a way that their revenues will at least cover their true cost of operation. Since cycle aging of battery cells represents a substantial part of this operating cost, the cost of battery degradation must be factored in these bids. However, existing models of battery degradation either do not fit market clearing software or do not reflect the actual battery aging mechanism. In this paper we model battery cycle aging using a piecewise linear cost function, an approach that provides a close approximation of the cycle aging mechanism of electrochemical batteries and can be incorporated easily into existing market dispatch programs. By defining the marginal aging cost of each battery cycle, we can assess the actual operating profitability of batteries. A case study demonstrates the effectiveness of the proposed model in maximizing the operating profit of a battery energy storage system taking part in the ISO New England energy and reserve markets

A first Experimental Investigation of the Practical Efficiency of Battery Scheduling

Nowadays, mobile devices are used more and more, and their battery lifetime is a key concern. In this paper, we concentrate on a method called battery scheduling with the aim to optimize the battery lifetime of mobile devices. This technique has already been largely theoretically studied in other papers. It consists, for systems containing multiple batteries, in switching the load from one battery to the other. Then, while following a given scheduling sequence, advantage can be taken from the recovery and rate capacity effects. However, little studies with experimental data of battery scheduling have been found. In this paper we describe a simple setup for measuring the possible gain of battery scheduling, and give some exploratory results for two types of real batteries: a smart Li-Ion battery used in the Thales personal communication system and a more commonly used NiCd battery. The results, so far, show that system lifetime extension is not systematic, and generally can only reach less then 10%

Computing lifetimes for battery-powered devices

The battery lifetime of mobile devices depends on the usage pattern of the battery, next to the discharge rate and the battery capacity. Therefore, it is important to include the usage pattern in battery lifetime computations. We do this by combining a stochastic workload, modeled as a continuous-time Markov model, with a well-known battery model. For this combined model, we provide new algorithms to efficiently compute the expected lifetime and the distribution and expected value of the delivered charge

Electric vehicle battery parameter identification and SOC observability analysis: NiMH and Li-S case studies

In this study, a framework is proposed for battery model identification to be applied in electric vehicle energy storage systems. The main advantage of the proposed approach is having capability to handle different battery chemistries. Two case studies are investigated: nickel-metal hydride (NiMH), which is a mature battery technology, and Lithium-Sulphur (Li-S), a promising next-generation technology. Equivalent circuit battery model parametrisation is performed in both cases using the Prediction-Error Minimization (PEM) algorithm applied to experimental data. The use of identified parameters for battery state-of-charge (SOC) estimation is then discussed. It is demonstrated that the set of parameters needed can change with a different battery chemistry. In the case of NiMH, the battery’s open circuit voltage (OCV) is adequate for SOC estimation. However, Li-S battery SOC estimation can be challenging due to the chemistry’s unique features and the SOC cannot be estimated from the OCV-SOC curve alone because of its flat gradient. An observability analysis demonstrates that Li-S battery SOC is not observable using the common state-space representations in the literature. Finally, the problem’s solution is discussed using the proposed framework

A study on battery model parametrisation problem: application-oriented trade-offs between accuracy and simplicity

This study is focused on fast low-fidelity battery modelling for online applications. Because the battery parameters change due to variations of battery’s states, the model may need to be updated during operation. This can be achieved through the use of an online parameter identification technique, making use of online current-voltage measurements. The parametrisation algorithm’s speed is a crucial issue in such applications. This paper describes a study exploring the trade-offs between speed and accuracy, considering equivalent circuit models with different levels of complexity and different parameter-fitting algorithms. A visual investigation of the battery parametrisation problem is also proposed by obtaining battery model identification surfaces which help us to avoid unnecessary complexities. Three standard fitting algorithms are used to parametrise battery models using current-voltage measurements. For each level of complexity, the algorithms performances are evaluated using experimental data from a small NiMH battery pack. An application-oriented view on this trade-offs is discussed which demonstrates that the final target of the battery parametrisation problem can significantly affect the choice of the fitting algorithm and battery model structur

Battery Modeling

The use of mobile devices is often limited by the capacity of the employed batteries. The battery lifetime determines how long one can use a device. Battery modeling can help to predict, and possibly extend this lifetime. Many different battery models have been developed over the years. However, with these models one can only compute lifetimes for specific discharge profiles, and not for workloads in general. In this paper, we give an overview of the different battery models that are available, and evaluate these models in their suitability to combine them with a workload model to create a more powerful battery model. \u