A Review of the Technical Challenges and Solutions in Maximising the Potential Use of Second Life Batteries from Electric Vehicles

Abstract

The increasing number of electric vehicles (EVs) on the roads has led to a rise in the number of batteries reaching the end of their first life. Such batteries, however, still have a capacity of 75–80% remaining, creating an opportunity for a second life in less power-intensive applications. Utilising these second-life batteries (SLBs) requires specific preparation, including grading the batteries based on their State of Health (SoH); repackaging, considering the end-use requirements; and the development of an accurate battery-management system (BMS) based on validated theoretical models. In this paper, we conduct a technical review of mathematical modelling and experimental analyses of SLBs to address existing challenges in BMS development. Our review reveals that most of the recent research focuses on environmental and economic aspects rather than technical challenges. The review suggests the use of equivalent-circuit models with 2RCs and 3RCs, which exhibit good accuracy for estimating the performance of lithium-ion batteries during their second life. Furthermore, electrochemical impedance spectroscopy (EIS) tests provide valuable information about the SLBs’ degradation history and conditions. For addressing calendar-ageing mechanisms, electrochemical models are suggested over empirical models due to their effectiveness and efficiency. Additionally, generating cycle-ageing test profiles based on real application scenarios using synthetic load data is recommended for reliable predictions. Artificial intelligence algorithms show promise in predicting SLB cycle-ageing fading parameters, offering significant time-saving benefits for lab testing. Our study emphasises the importance of focusing on technical challenges to facilitate the effective utilisation of SLBs in stationary applications, such as building energy-storage systems and EV charging stations.

Fuzzy Logic-Based Energy Management System for Regenerative Braking of Electric Vehicles with Hybrid Energy Storage System

“…Various battery types are used in EVs, but the most promising, and currently the most common type of energy storage, is the lithium-ion (Li-ion) battery [24,25]. While Li-ion batteries have a high energy density, their power density is not high enough [26].…”

State of Health Prediction of Electric Vehicles’ Retired Batteries Based on First-Life Historical Degradation Data Using Predictive Time-Series Algorithms

“…These methods are sensitive to measurement noise and operating temperature, and require low current rates, limiting their applications [24]. Model-based methods use equivalent circuit models (ECM) or electrochemical models (EM) combined with advanced filter techniques like the Kalman filter (KF) or particle filter (PF) to calculate the battery degradation parameters for estimating the SoH [25]. Although these models can accurately reflect the dynamic characteristics of a battery, the complex internal principles and working environment make it challenging to establish a suitable battery model.…”

A Method for Assessing the Technical Condition of Traction Batteries Using the Metalog Family of Probability Distributions

“…If the repair processes are not able to restore higher performance and the traction battery is still functional and safe, it may have other uses. The second life of a traction battery can usually be in the form of a stationary energy storage device [77,78]. In this application, the maximum energy capacity is not as important as in a vehicle, where it directly affects the range of the electric vehicle.…”