Pin Jern Ker
Other affiliations: University of Sheffield
Bio: Pin Jern Ker is an academic researcher from Universiti Tenaga Nasional. The author has contributed to research in topics: Avalanche photodiode & Microgrid. The author has an hindex of 22, co-authored 119 publications receiving 1911 citations. Previous affiliations of Pin Jern Ker include University of Sheffield.
TL;DR: This review will hopefully lead to increasing efforts toward the development of an advanced Li-ion battery in terms of economics, longevity, specific power, energy density, safety, and performance in vehicle applications.
Abstract: A variety of rechargeable batteries are now available in world markets for powering electric vehicles (EVs). The lithium-ion (Li-ion) battery is considered the best among all battery types and cells because of its superior characteristics and performance. The positive environmental impacts and recycling potential of lithium batteries have influenced the development of new research for improving Li-ion battery technologies. However, the cost reduction, safe operation, and mitigation of negative ecological impacts are now a common concern for advancement. This paper provides a comprehensive study on the state of the art of Li-ion batteries including the fundamentals, structures, and overall performance evaluations of different types of lithium batteries. A study on a battery management system for Li-ion battery storage in EV applications is demonstrated, which includes a cell condition monitoring, charge, and discharge control, states estimation, protection and equalization, temperature control and heat management, battery fault diagnosis, and assessment aimed at enhancing the overall performance of the system. It is observed that the Li-ion batteries are becoming very popular in vehicle applications due to price reductions and lightweight with high power density. However, the management of the charging and discharging processes, CO2 and greenhouse gases emissions, health effects, and recycling and refurbishing processes have still not been resolved satisfactorily. Consequently, this review focuses on the many factors, challenges, and problems and provides recommendations for sustainable battery manufacturing for future EVs. This review will hopefully lead to increasing efforts toward the development of an advanced Li-ion battery in terms of economics, longevity, specific power, energy density, safety, and performance in vehicle applications.
TL;DR: The goal of this paper is to comprehensively review the different estimation models to predict SOH, and RUL in a comparative manner and identify the classifications, characteristics and evaluation processes with advantages and disadvantages for EV applications.
Abstract: Electric vehicles (EVs) have become increasingly popular due to zero carbon emission, reduction of fossil fuel reserve, comfortable and light transport However, EVs employing lithium-ion battery are facing difficulties in terms of predicting accurate health and remaining useful life states due to various internal and external factors Currently, very few papers are addressed to summarize the state of health (SOH) and remaining useful life (RUL) estimation approaches In this regard, the goal of this paper is to comprehensively review the different estimation models to predict SOH, and RUL in a comparative manner The results identify the classifications, characteristics and evaluation processes with advantages and disadvantages for EV applications The review also investigates the issues and challenges with possible solutions Furthermore, the review provides some selective proposals for the further technological development of SOH, and RUL estimation for lithium-ion batteries All the highlights insight this review will hopefully lead to the increasing efforts towards the development of the advanced SOH and RUL methods for future EV uses
TL;DR: An advanced ESS is required with regard to capacity, protection, control interface, energy management, and characteristics to enhance the performance of ESS in MG applications to develop a cost-effective and efficient ESS model with a prolonged life cycle for sustainable MG implementation.
Abstract: A microgrid (MG) is a local entity that consists of distributed energy resources (DERs) to achieve local power reliability and sustainable energy utilization. The MG concept or renewable energy technologies integrated with energy storage systems (ESS) have gained increasing interest and popularity because it can store energy at off-peak hours and supply energy at peak hours. However, existing ESS technology faces challenges in storing energy due to various issues, such as charging/discharging, safety, reliability, size, cost, life cycle, and overall management. Thus, an advanced ESS is required with regard to capacity, protection, control interface, energy management, and characteristics to enhance the performance of ESS in MG applications. This paper comprehensively reviews the types of ESS technologies, ESS structures along with their configurations, classifications, features, energy conversion, and evaluation process. Moreover, details on the advantages and disadvantages of ESS in MG applications have been analyzed based on the process of energy formations, material selection, power transfer mechanism, capacity, efficiency, and cycle period. Existing reviews critically demonstrate the current technologies for ESS in MG applications. However, the optimum management of ESSs for efficient MG operation remains a challenge in modern power system networks. This review also highlights the key factors, issues, and challenges with possible recommendations for the further development of ESS in future MG applications. All the highlighted insights of this review significantly contribute to the increasing effort toward the development of a cost-effective and efficient ESS model with a prolonged life cycle for sustainable MG implementation.
TL;DR: This review presents the recent SOC estimation methods highlighting the model-based and data-driven approaches and delivers potential recommendations for the development of SOC estimation method of lithium-ion battery in EV applications.
Abstract: 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.
TL;DR: This paper focuses on the various factors and challenges of existing optimization algorithms, hydrogen fuel source, environment and safety, and economical and societal concerns, as well as provides recommendations for designing capable and efficient EMSs for FCHEVs.
Abstract: Hybrid electric vehicle technologies emerge mainly because of the instability in fossil fuel prices, resources and the terrible impact of global warming. As most transport systems use fossil fuel and emit greenhouse gases, many researchers have studied the potential of fuel-cell hybrid electric vehicles (FCHEVs). FCHEVs are vehicles with zero greenhouse gas emission because they only depend on hydrogen. Numerous studies have proven that fuel cells with energy storage elements can provide sufficient energy required by FCHEVs. However, end users demand FCHEVs that are not only efficient in delivering the energy required but can also optimize hydrogen consumption and prolong battery lifetime to compete with current internal combustion engine vehicles. Therefore, advanced optimization algorithms for an FCHEV energy management system (EMS) must be developed to improve the performance efficiency of FCHEVs. This paper presents a critical review of the different types of FCHEV EMSs and their optimization algorithms to solve existing limitations and enhance the performance of future FCHEVs. Consequently, a comprehensive review on the major categories of FCHEV EMSs, such as proportional–integral–derivative controller, operational or state mode, rule-based or fuzzy logic, and equivalent consumption minimization strategies, are explained. This paper also describes optimization techniques such as linear programming, dynamic programming, Pontryagin’s minimum principle, genetic algorithm, particle swarm optimization and rule-based logic optimization for the EMSs of FCHEVs. Furthermore, it focuses on the various factors and challenges of existing optimization algorithms, hydrogen fuel source, environment and safety, and economical and societal concerns, as well as provides recommendations for designing capable and efficient EMSs for FCHEVs. All the highlighted insights of this review will hopefully lead to increasing efforts toward the development of an advanced optimization algorithm for future FCHEV EMSs.
TL;DR: This review categorises data-driven battery health estimation methods according to their underlying models/algorithms and discusses their advantages and limitations, then focuses on challenges of real-time battery health management and discuss potential next-generation techniques.
Abstract: Accurate health estimation and lifetime prediction of lithium-ion batteries are crucial for durable electric vehicles. Early detection of inadequate performance facilitates timely maintenance of battery systems. This reduces operational costs and prevents accidents and malfunctions. Recent advancements in “Big Data” analytics and related statistical/computational tools raised interest in data-driven battery health estimation. Here, we will review these in view of their feasibility and cost-effectiveness in dealing with battery health in real-world applications. We categorise these methods according to their underlying models/algorithms and discuss their advantages and limitations. In the final section we focus on challenges of real-time battery health management and discuss potential next-generation techniques. We are confident that this review will inform commercial technology choices and academic research agendas alike, thus boosting progress in data-driven battery health estimation and prediction on all technology readiness levels.
TL;DR: In this article, a systematic review of the most commonly used battery modeling and state estimation approaches for BMSs is presented, including the physics-based electrochemical models, the integral and fractional order equivalent circuit models, and data-driven models.
Abstract: With the rapid development of new energy electric vehicles and smart grids, the demand for batteries is increasing. The battery management system (BMS) plays a crucial role in the battery-powered energy storage system. This paper presents a systematic review of the most commonly used battery modeling and state estimation approaches for BMSs. The models include the physics-based electrochemical models, the integral and fractional order equivalent circuit models, and data-driven models. The state estimation approaches are analyzed from the perspectives of remaining capacity and energy estimation, power capability prediction, lifespan and health prognoses, and other crucial indexes in BMS. This present paper, through the analysis of literature, includes almost all states in the BMS. The estimation approaches of state-of-charge (SOC), state-of-energy (SOE), state-of-power (SOP), state-of-function (SOF), state-of-health (SOH), remaining useful life (RUL), remaining discharge time (RDT), state-of-balance (SOB), and state-of-temperature (SOT) are reviewed and discussed in a systematical way. Moreover, the challenges and outlooks of the research on future battery management are disclosed, in the hope of providing some inspirations to the development and design of the next-generation BMSs.
TL;DR: A timely and comprehensive review of the battery lifetime prognostic technologies with a focus on recent advances in model-based, data-driven, and hybrid approaches is presented, analyzed, and compared.
Abstract: Summary Lithium-ion batteries have been widely used in many important applications. However, there are still many challenges facing lithium-ion batteries, one of them being degradation. Battery degradation is a complex problem, which involves many electrochemical side reactions in anode, electrolyte, and cathode. Operating conditions affect degradation significantly and therefore the battery lifetime. It is of extreme importance to achieve accurate predictions of the remaining battery lifetime under various operating conditions. This is essential for the battery management system to ensure reliable operation and timely maintenance and is also critical for battery second-life applications. After introducing the degradation mechanisms, this paper provides a timely and comprehensive review of the battery lifetime prognostic technologies with a focus on recent advances in model-based, data-driven, and hybrid approaches. The details, advantages, and limitations of these approaches are presented, analyzed, and compared. Future trends are presented, and key challenges and opportunities are discussed.