• Energy Research

Electric vehicle (EV) batteries, i.e., currently almost exclusively lithium-ion batteries, are removed from the vehicle once they no longer meet certain requirements. However, instead of being disposed of or recycled, the removed batteries can be used in another, less demanding application, giving them a “second life”. Research in the field of second-life batteries (SLBs) is still at an early stage and, to better understand the “second life” concept and the related challenges, potential second-life applications need to be identified first. Using a detailed study of the scientific literature and an interview with field experts, a list of potential second-life applications was drafted. Afterwards, a technical, economic, and legal evaluation was conducted to identify the most promising options. The findings of this research consisted of the identification of 65 different mobile, semi-stationary and stationary second-life applications; the applications selected as most promising are automated guided vehicles (AGVs) and industrial energy storage systems (ESSs) with renewable firming purposes. This research confirms the great potential of SLBs indicating that second-life applications are many and belong to a broad spectrum of different sectors. The applications identified as most promising are particularly attractive for the second-life use of batteries as they belong to fast-growing markets.

Electric vehicle (EV) batteries, i.e., currently almost exclusively lithium-ion batteries, are removed from the vehicle once they no longer meet certain requirements. However, instead of being disposed of or recycled, the removed batteries can be used in another, less demanding application, giving them a “second life”. Research in the field of second-life batteries (SLBs) is still at an early stage and, to better understand the “second life” concept and the related challenges, potential second-life applications need to be identified first. Using a detailed study of the scientific literature and an interview with field experts, a list of potential second-life applications was drafted. Afterwards, a technical, economic, and legal evaluation was conducted to identify the most promising options. The findings of this research consisted of the identification of 65 different mobile, semi-stationary and stationary second-life applications; the applications selected as most promising are automated guided vehicles (AGVs) and industrial energy storage systems (ESSs) with renewable firming purposes. This research confirms the great potential of SLBs indicating that second-life applications are many and belong to a broad spectrum of different sectors. The applications identified as most promising are particularly attractive for the second-life use of batteries as they belong to fast-growing markets.

The knowledge of the influence of high dynamic loads on the electrical and mechanical behavior of lithium-ion cells is of high importance to ensure a safe use of batteries over the lifetime in electric vehicles. For the first time, the behavior of six commercial Li-Ion pouch cells after a constrained short-time acceleration (300 g over 6 ms) with a resulting cell surface pressure of 9.37 MPa was investigated. At this load, two out of six cells suffered from an internal short circuit, showing several damaged separator layers across the thickness in the area of the cell tabs. For the cells that remained intact, a range of measurement techniques (e.g., inner resistance measurement, electrochemical impedance spectroscopy (EIS), or thermal imaging) was used to reveal changes in the electrical property resulting from the load. The cells without short circuit show an increase of internal resistance (average of 0.89%) after the dynamic pre-load. The electric circuit model based on the EIS measurement indicates a decrease of the resistance R1 up to 30.8%. Additionally, mechanical properties of the cells in an abuse test subsequent to the dynamic pre-load were significantly influenced. The pre-loaded cell could sustain an 18% higher intrusion depth before electrical failure occurred as compared to a fresh cell in an indentation test. The results of this study revealed that a high acceleration pulse under realistic boundary conditions can lead to critical changes in a battery cell’s properties and needs to be taken into account for future safety assessments.

The knowledge of the influence of high dynamic loads on the electrical and mechanical behavior of lithium-ion cells is of high importance to ensure a safe use of batteries over the lifetime in electric vehicles. For the first time, the behavior of six commercial Li-Ion pouch cells after a constrained short-time acceleration (300 g over 6 ms) with a resulting cell surface pressure of 9.37 MPa was investigated. At this load, two out of six cells suffered from an internal short circuit, showing several damaged separator layers across the thickness in the area of the cell tabs. For the cells that remained intact, a range of measurement techniques (e.g., inner resistance measurement, electrochemical impedance spectroscopy (EIS), or thermal imaging) was used to reveal changes in the electrical property resulting from the load. The cells without short circuit show an increase of internal resistance (average of 0.89%) after the dynamic pre-load. The electric circuit model based on the EIS measurement indicates a decrease of the resistance R1 up to 30.8%. Additionally, mechanical properties of the cells in an abuse test subsequent to the dynamic pre-load were significantly influenced. The pre-loaded cell could sustain an 18% higher intrusion depth before electrical failure occurred as compared to a fresh cell in an indentation test. The results of this study revealed that a high acceleration pulse under realistic boundary conditions can lead to critical changes in a battery cell’s properties and needs to be taken into account for future safety assessments.

The crash safety of lithium-ion traction batteries is a relevant concern for electric vehicles. Current passive safety strategies of traction batteries usually come at the cost of their volumetric or gravimetric energy density. This work analyses the influence of the variables cell selection and orientation within the traction battery on the crash safety of an electric-powered two-wheeler. These two variables do not negatively influence the traction battery’s volumetric or gravimetric energy density in the design process. Metamodels and numerical simulations are used to evaluate the crash safety of an electric-powered two-wheeler’s traction battery in a potentially dangerous crash scenario. The influence of the variable’s cell selection and orientation is evaluated through the internal short circuit risk of the integrated cells. The comparison of the metamodels shows that the cell orientation reduces the internal short circuit risk by up to 51% on average in the analysed crash scenario. The cell selection reduces it only up to 21% on average. The results show that crash safety can be increased in the design process, and a combination with the current protection strategies can increase crash safety further.

The crash safety of lithium-ion traction batteries is a relevant concern for electric vehicles. Current passive safety strategies of traction batteries usually come at the cost of their volumetric or gravimetric energy density. This work analyses the influence of the variables cell selection and orientation within the traction battery on the crash safety of an electric-powered two-wheeler. These two variables do not negatively influence the traction battery’s volumetric or gravimetric energy density in the design process. Metamodels and numerical simulations are used to evaluate the crash safety of an electric-powered two-wheeler’s traction battery in a potentially dangerous crash scenario. The influence of the variable’s cell selection and orientation is evaluated through the internal short circuit risk of the integrated cells. The comparison of the metamodels shows that the cell orientation reduces the internal short circuit risk by up to 51% on average in the analysed crash scenario. The cell selection reduces it only up to 21% on average. The results show that crash safety can be increased in the design process, and a combination with the current protection strategies can increase crash safety further.

The safety of lithium-ion batteries within electrified vehicles plays an important role. Hazards can arise from contaminated batteries resulting from non-obvious damages or insufficient production processes. A systematic examination requires experimental methods to provoke a defined contamination. Two prerequisites were required: First, the extent and type of contamination should be determinable to exclude randomness. Second, specimens should work properly before the contamination, enabling realistic behavior. In this study, two experimental methods were developed to allow for the first time a controlled and reproducible application of water or oxygen into 11 single-layer full cells (Li4Ti5O12/LiCoO2) used as specimens during electrical cycling. Electrochemical impedance spectroscopy was used to continuously monitor the specimens and to fit the parameters of an equivalent circuit model (ECM). For the first time, these parameters were used to calibrate a machine-learning algorithm which was able to predict the contamination state. A decision tree was calibrated with the ECM parameters of eight specimens (training data) and was validated by predicting the contamination state of the three remaining specimens (test data). The prediction quality proved the usability of classification algorithms to monitor for contaminations or non-obvious battery damage after manufacturing and during use. It can be an integral part of battery management systems that increases vehicle safety.

The safety of lithium-ion batteries within electrified vehicles plays an important role. Hazards can arise from contaminated batteries resulting from non-obvious damages or insufficient production processes. A systematic examination requires experimental methods to provoke a defined contamination. Two prerequisites were required: First, the extent and type of contamination should be determinable to exclude randomness. Second, specimens should work properly before the contamination, enabling realistic behavior. In this study, two experimental methods were developed to allow for the first time a controlled and reproducible application of water or oxygen into 11 single-layer full cells (Li4Ti5O12/LiCoO2) used as specimens during electrical cycling. Electrochemical impedance spectroscopy was used to continuously monitor the specimens and to fit the parameters of an equivalent circuit model (ECM). For the first time, these parameters were used to calibrate a machine-learning algorithm which was able to predict the contamination state. A decision tree was calibrated with the ECM parameters of eight specimens (training data) and was validated by predicting the contamination state of the three remaining specimens (test data). The prediction quality proved the usability of classification algorithms to monitor for contaminations or non-obvious battery damage after manufacturing and during use. It can be an integral part of battery management systems that increases vehicle safety.