Lithium-ion battery is an appropriate choice for electric vehicle (EV) due to its promising features of high voltage, high energy density, low self-discharge and long lifecycles. The successful operation of EV is highly dependent on the operation of battery management system (BMS). State of charge (SOC) is one of the vital paraments of BMS which signifies the amount of charge left in a battery. A good estimation of SOC leads to long battery life and prevention of catastrophe from battery failure. Besides, an accurate and robust SOC estimation has great significance towards an efficient EV operation. However, SOC estimation is a complex process due to its dependency on various factors such as battery age, ambient temperature, and many unknown factors. This review presents the recent SOC estimation methods highlighting the model-based and data-driven approaches. Model-based methods attempt to model the battery behavior incorporating various factors into complex mathematical equations in order to accurately estimate the SOC while the data-driven methods adopt an approach of learning the battery's behavior by running complex algorithms with a large amount of measured battery data. The classifications of model-based and data-driven based SOC estimation are explained in terms of estimation model/algorithm, benefits, drawbacks, and estimation error. In addition, the review highlights many factors and challenges and delivers potential recommendations for the development of SOC estimation methods in EV applications. All the highlighted insights of this review will hopefully lead to increased efforts toward the enhancement of SOC estimation method of lithium-ion battery for the future high-tech EV applications.

http://dspace.uniten.edu.my/jspui/bitstream/123456789/13358/1/State%20of%20Charge%20Estimation%20for%20Lithium-Ion%20Batteries%20Using%20Model-Based%20and%20Data-Driven%20Methods%20A%20Review.pdf

State of Charge Estimation for Lithium-Ion Batteries Using Model-Based and Data-Driven Methods: A Review

Intelligent algorithms and control strategies for battery management system in electric vehicles: Progress, challenges and future outlook

In this era of a sustainable energy revolution, energy storage in batteries has come up as one of the most emerging fields. Today, the battery usage is outracing in e-vehicles. With the increase in the usage of batteries, efficient energy storage, and retrieval in the batteries has come to the foreground. Further, along with a few other parameters, the operating temperature of the battery of an electric vehicle plays a vital role in its performance. Also, the internal heat generation limits the performance of the lithium-ion batteries. The operating temperature range of an electric vehicle lithium-ion battery ranges from 15°C to 35°C and this is being achieved by a battery thermal management system (BTMS). Owing to the efficiency of these systems, a considerable amount of work has been performed beforehand. To take this research forward, this paper gives a comprehensive review of all the experimental and numerical analyses conducted on various BTMS techniques for electric and hybrid vehicles where the battery cooling systems with air, liquid, phase change material, heat pipe, refrigeration cooling methods are discussed. The significant findings and outcomes of the experimental, simulation, and modeling work on BTMS in recent past years are reviewed in depth. Besides that, a systematic review of hybrid battery cooling systems is also presented in this paper. Lastly, a summary is made of all the developed BTMS along with their experimental, mathematical, and computational simulation models.

Developments in battery thermal management systems for electric vehicles: A technical review

A review on passive and active strategies of enhancing the safety of lithium-ion batteries

The ongoing electrification of mobility comes with the challenge of charging electric vehicles (EVs) sufficiently while charging infrastructure capacities are limited. Smart charging algorithms produce charge plans for individual EVs and aim to assign charging capacities fairly and efficiently between vehicles in a fleet. In practice, EV charging processes follow nonlinear charge profiles such as constant-current, constant-voltage (CCCV). Smart charging must consider charge profiles in order to avoid gaps between charge plans and actual EV power consumption. Generally valid models of charge profiles and their parameters for a diverse set of EVs are not publicly available. In this work we propose a data-driven approach for integrating a machine learning model to predict arbitrary charge profiles into a smart charging algorithm. We train machine learning models with a dataset consisting of charging processes from the workplace gathered in 2016–2018 from a heterogeneous EV fleet of 1001 EVs with 18 unique models. Each charging process includes the time series of charging power. After preprocessing, the dataset contains 10.595 charging processes leading to 1.2 million data points in total. We then compare different machine learning models for charge profile predictions finding that XGBoost yields the most accurate predictions with a mean absolute error (MAE) of 126W and a relative MAE of 0.06. Simulations show that smart charging with the integrated XGBoost model leads to a more effective infrastructure usage with up to 21% more energy charged compared to smart charging without considering charge profiles. Furthermore, an ablation study on regression model features shows the EV’s model is not a necessary attribute for accurate charge profile predictions. However, charging features are required including the number of phases used for charging.

Data-driven smart charging for heterogeneous electric vehicle fleets

Towards a smarter battery management system: A critical review on optimal charging methods of lithium ion batteries

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Towards a smarter battery management system: A critical review on optimal charging methods of lithium ion batteries