An overview of new and current developments in state of charge (SOC) estimating methods for battery is given where the focus lies upon mathematical principles and practical implementations. As the battery SOC is an important parameter, which reflects the battery performance, so accurate estimation of SOC cannot only protect battery, prevent overcharge or discharge, and improve the battery life, but also let the application make rationally control strategies to achieve the purpose of saving energy. This paper gives a literature survey on the categories and mathematical methods of SOC estimation. Based on the assessment of SOC estimation methods, the future development direction of SOC estimation is proposed.

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The State of Charge Estimating Methods for Battery: A Review

Investigating the error sources of the online state of charge estimation methods for lithium-ion batteries in electric vehicles

A hybrid generator with a photovoltaic energy conversion system is proposed with supercapacitors and lead-acid batteries in a dc-coupled structure. The objective of this system is to supply the prescribed reactive and active power to the grid. This paper focuses on the strategy, which makes it possible to ensure a high battery state of charge and overcharge security by designing a dedicated local control system. A continuous dynamic model and a control design of the power system studied are proposed in this paper. Simulation and experimental results illustrate the performances obtained.

Power Control Design of a Battery Charger in a Hybrid Active PV Generator for Load-Following Applications

In recent years, a significant interest in hybrid and battery electric vehicles has arisen globally due to reducing fuel consumption, mitigating dependence on imported oil and decreasing greenhouse gas emissions. The overall success of these vehicles mostly depends on the performance of sub-systems that they are created. In order to enhance the performances of these sub-systems, estimation of their parameters with high accuracy is required. Furthermore, estimation strategies play an important role in battery management, vehicle energy management and vehicle control by undertaking different tasks. There have been a limited number of review studies related with estimation strategies that are only focused on battery state of charge (SOC) and state of health (SOH) estimation. This paper presents a comprehensive review on various estimation strategies used in hybrid and battery electric vehicles for the first time in the literature. The existing estimation strategies are classified and different methodologies used in each estimation strategy are elaborated. Recent research advances on existing estimation strategies are clearly emphasized by reviewing numerous studies over 200 papers.

A comprehensive review on estimation strategies used in hybrid and battery electric vehicles

To fulfill reliable battery management in electric vehicles (EVs), an advanced State-of-Charge (SOC) estimator is developed via machine learning methodology. A novel genetic algorithm-based fuzzy C-means (FCM) clustering technique is first used to partition the training data sampled in the driving cycle-based test of a lithium-ion battery. The clustering result is applied to learn the topology and antecedent parameters of the model. Recursive least-squares algorithm is then employed to extract its consequent parameters. To ensure good accuracy and resilience, the backpropagation learning algorithm is finally adopted to simultaneously optimize both the antecedent and consequent parts. Experimental results verify that the proposed estimator exhibits sufficient accuracy and outperforms those built by conventional fuzzy modeling methods.

Feng Wang

Papers

Combined state of charge estimator for electric vehicle battery pack