Lithium-ion battery packs in hybrid and electric vehicles, as well as in other traction applications, are always equipped with a Battery Management System (BMS). The BMS consists of hardware and software for battery management including, among others, algorithms determining battery states. The accurate and reliable State of Health (SOH) estimation is a challenging issue and it is a core factor of a battery energy storage system. In this paper, battery SOH monitoring methods are reviewed. To this end, different scientific and technical literature is studied and the respective approaches are classified in specific groups. The groups are organized in terms of the way the method is carried out: Experimental Techniques or Adaptive Models. Not only strengths and weaknesses for the use in online BMS applications are reviewed but also their accuracy and precision is studied. At the end of the document a potential, new and promising via in order to develop a methodology to estimate the SOH in real applications is detailed.

Critical review of state of health estimation methods of Li-ion batteries for real applications

Lithium iron phosphate based battery: Assessment of the aging parameters and development of cycle life model

State-of-health estimation for Li-ion batteries by combing the incremental capacity analysis method with grey relational analysis

To cope with the new transportation challenges and to ensure the safety and durability of electric vehicles and hybrid electric vehicles, high performance and reliable battery health management systems are required. The Battery State of Health (SOH) provides critical information about its performances, its lifetime and allows a better energy management in hybrid systems. Several research studies have provided different methods that estimate the battery SOH. Yet, not all these methods meet the requirement of automotive real-time applications. The real time estimation of battery SOH is important regarding battery fault diagnosis. Moreover, being able to estimate the SOH in real time ensure an accurate State of Charge and State of Power estimation for the battery, which are critical states in hybrid applications. This study provides a review of the main battery SOH estimation methods, enlightening their main advantages and pointing out their limitations in terms of real time automotive compatibility and especially hybrid electric applications. Experimental validation of an online and on-board suited SOH estimation method using model-based adaptive filtering is conducted to demonstrate its real-time feasibility and accuracy.

https://hal.archives-ouvertes.fr/hal-02993901/document

A review of battery state of health estimation methods: hybrid electric vehicle challenges

It is estimated that by the year 2030, the cumulative of Electric Vehicles (EVs) will reach 85 million. Once EV batteries degraded to 70–80% of their initial capacity, EV owners will have to replace the EV’s batteries as the residual capacity becomes insufficient for automotive use. As a result, more batteries will be discarded from EVs. These batteries could be re-purposed in other applications, where they are known as the EV Second Life Batteries (SLB). In this paper, several projects and research works are reviewed to understand the up-to-date state-of-the-art related to SLB. The technical feasibility, economics, and environmental impact of using SLB are investigated. Different applications of SLB, as well as the assessment and testing required before re-purposing EV batteries, are presented. Some of the existing projects related to SLB, such as the studies done in many countries, batteries' types, applications, and scope of the study, have been summarised. It was found that utilising SLB addresses not only an environmental concern with regards to the discarded batteries but also provides an excellent opportunity to generate revenue if assessed and used optimally. Nevertheless, some challenges do exist, such as the lack of standardised assessment and lack of reliable information due to the low number of studies related to SLB. Further studies of SLB, which could help understand the feasibility and economics of using them and standardising their assessment, are recommended.

Feasibility of utilising second life EV batteries: Applications, lifespan, economics, environmental impact, assessment, and challenges

According to the characteristic analysis of lithium-ion power battery, battery accelerate life test is carried out to obtain the relevant conclusions such as the changing trend of battery ohmic resistance in different conditions. Battery ohmic resistance is consequently set up as the Evaluation Index of lifetime. Battery ohmic resistance equivalent model is established aiming at the real-time identification of lithium-ion battery ohmic resistance. Based on this model, the real-time identification is achieved by the experimental collected data and recursive least squares algorithm. The adoption of battery ohmic resistance identification algorithm can be applied to provide some useful reference for the battery life state estimation.

Wei Xu

Papers

Internal Resistance Identification in Vehicle Power Lithium-Ion Battery and Application in Lifetime Evaluation