The negative impact of the automotive industry on climate change can be tackled by changing from fossil driven vehicles towards battery electric vehicles with no tailpipe emissions. However their adoption mainly depends on the willingness to pay for the extra cost of the traction battery. The goal of this paper is to predict the cost of a battery pack in 2030 when considering two aspects: firstly a decade of research will ensure an improvement in material sciences altering a battery’s chemical composition. Secondly by considering the price erosion due to the production cost optimization, by maturing of the market and by evolving towards to a mass-manufacturing situation. The cost of a lithium Nickel Manganese Cobalt Oxide (NMC) battery (Cathode: NMC 6:2:2 ; Anode: graphite) as well as silicon based lithium-ion battery (Cathode: NMC 6:2:2 ; Anode: silicon alloy), expected to be on the market in 10 years, will be predicted to tackle the first aspect. The second aspect will be considered by combining process-based cost calculations with learning curves, which takes the increasing battery market into account. The 100 dollar/kWh sales barrier will be reached respectively between 2020-2025 for silicon based lithium-ion batteries and 2025–2030 for NMC batteries, which will give a boost to global electric vehicle adoption.

Reusing electric vehicle batteries once they have been retired from the automotive application is stated as one of the possible solutions to reduce electric vehicle costs. Many publications in literature have analysed the economic viability of such a solution, and some car manufacturers have recently started running several projects to demonstrate the technical viability of the so-called battery second life. Nevertheless, the degradation behaviour of second life batteries remains unknown and represents one of the biggest gaps in the literature. The present work aims at evaluating the effects of Lithium-ion (Li-ion) NMC/C battery State of Health (SOH) and ageing history over the second life performance, on two different applications: a residential demand management application and a power smoothing renewable integration application. The performance and degradation of second life batteries are assessed both at the cell level and at stack level. Homogeneous and heterogeneous stacks are analysed in order to evaluate the impact of cell-to-cell history and SOH differences over the battery performance and degradation behaviour. Results obtained showed a strong influence of the first life battery ageing history upon the second life battery performance and degradation. Thereby, proper monitoring and battery selection appears crucial to certify the technical viability of battery second life.

In this paper, the performances of various lithium-ion chemistries for use in plug-in hybrid electric vehicles have been investigated and compared to several other rechargeable energy storage systems technologies such as lead-acid, nickel-metal hydride and electrical-double layer capacitors. The analysis has shown the beneficial properties of lithium-ion in the terms of energy density, power density and rate capabilities. Particularly, the nickel manganese cobalt oxide cathode stands out with the high energy density up to 160 Wh/kg, compared to 70–110, 90 and 71 Wh/kg for lithium iron phosphate cathode, lithium nickel cobalt aluminum cathode and, lithium titanate oxide anode battery cells, respectively. These values are considerably higher than the lead-acid (23–28 Wh/kg) and nickel-metal hydride (44–53 Wh/kg) battery technologies. The dynamic discharge performance test shows that the energy efficiency of the lithium-ion batteries is significantly higher than the lead-acid and nickel-metal hydride technologies. The efficiency varies between 86% and 98%, with the best values obtained by pouch battery cells, ahead of cylindrical and prismatic battery design concepts. Also the power capacity of lithium-ion technology is superior compared to other technologies. The power density is in the range of 300–2400 W/kg against 200–400 and 90–120 W/kg for lead-acid and nickel-metal hydride, respectively. However, considering the influence of energy efficiency, the power density is in the range of 100–1150 W/kg. Lithium-ion batteries optimized for high energy are at the lower end of this range and are challenged to meet the United States Advanced Battery Consortium, SuperLIB and Massachusetts Institute of Technology goals. Their association with electric-double layer capacitors, which have low energy density (4–6 Wh/kg) but outstanding power capabilities, could be very interesting. The study of the rate capability of the lithium-ion batteries has allowed for a new state of charge estimation, encompassing all essential performance parameters. The rate capabilities tests are reflected by Peukert constants, which are significantly lower for lithium-ion batteries than for nickel-metal hydride and lead-acid. Furthermore, rate capabilities during charging have been investigated. Lithium-ion batteries are able to store about 80% of the capacity at current rate 2I_t, with high power cells accepting over 90%. At higher charging rates of 5I_t or more, the internal resistance impedes charge acceptance by high energy cells. The lithium titanate anode, due to its high surface area (100 m²/g compared to 3 m²/g for the graphite based anode) performs much better in this respect. The behavior of lithium-ion batteries has been investigated at different conditions. The analysis has leaded us to a new lithium ion battery model. This model will be compared to existing battery models in future research contributions.

Accurate modeling of the nonlinear relationship between the open circuit voltage (OCV) and the state of charge (SOC) is required for adaptive SOC estimation during the lithium-ion (Li-ion) battery operation. Online SOC estimation should meet several constraints, such as the computational cost, the number of parameters, as well as the accuracy of the model. In this paper, these challenges are considered by proposing an improved simplified and accurate OCV model of a nickel manganese cobalt (NMC) Li-ion battery, based on an empirical analytical characterization approach. In fact, composed of double exponential and simple quadratic functions containing only five parameters, the proposed model accurately follows the experimental curve with a minor fitting error of 1 mV. The model is also valid at a wide temperature range and takes into account the voltage hysteresis of the OCV. Using this model in SOC estimation by the extended Kalman filter (EKF) contributes to minimizing the execution time and to reducing the SOC estimation error to only 3% compared to other existing models where the estimation error is about 5%. Experiments are also performed to prove that the proposed OCV model incorporated in the EKF estimator exhibits good reliability and precision under various loading profiles and temperatures.

The non-uniform surface temperature distribution of a battery cell results from complex reactions inside the cell and makes efficient thermal management a challenging task. This experimental work attempts to determine the evolution of surface temperature distribution of three pouch type commercial cells: Nickel Manganese Cobalt oxide (NMC)-based 20 Ah cell, Lithium Iron Phosphate (LFP) 14 Ah, and Lithium Titanate Oxide (LTO) 5 Ah battery cell by using contact thermistor and infrared (IR) thermography. High current (up to 100 A) continuous charge/discharge and high current (80 A) micro pulse cycling profile were applied on the cells. It was found that thermistor based temperature profile varied cell to cell, especially the LTO cell. Among the investigated cells, the NMC cell shows highest temperature rise and the LTO cell the lowest rise. IR (Infrared) images revealed the spatial distribution of surface temperature, in particular the location of the hottest region varies depending not only on the geometrical and material properties of the cell, but also the type of loads applied on the cells. Finally, a modeling perspective of the cell temperature non-uniformity is also discussed.

In this paper, advanced equivalent circuit models (ECMs) were developed to model large format and high energy nickel manganese cobalt (NMC) lithium-ion 20 Ah battery cells. Different temperatures conditions, cell characterization test (Normal and Advanced Tests), ECM topologies (1st and 2nd Order Thévenin model), state of charge (SoC) estimation techniques (Coulomb counting and extended Kalman filtering) and validation profiles (dynamic discharge pulse test (DDPT) and world harmonized light vehicle profiles) have been incorporated in the analysis. A concise state-of-the-art of different lithium-ion battery models existing in the academia and industry is presented providing information about model classification and information about electrical models. Moreover, an overview of the different steps and information needed to be able to create an ECM model is provided. A comparison between begin of life (BoL) and aged (95%, 90% state of health) ECM parameters (internal resistance (R_o), polarization resistance (R_p), activation resistance (R_p2) and time constants (τ) is presented. By comparing the BoL to the aged parameters an overview of the behavior of the parameters is introduced and provides the appropriate platform for future research in electrical modeling of battery cells covering the ageing aspect. Based on the BoL parameters 1st and 2nd order models were developed for a range of temperatures (15 °C, 25 °C, 35 °C, 45 °C). The highest impact to the accuracy of the model (validation results) is the temperature condition that the model was developed. The 1st and 2nd order Thévenin models and the change from normal to advanced characterization datasets, while they affect the accuracy of the model they mostly help in dealing with high and low SoC linearity problems. The 2nd order Thévenin model with advanced characterization parameters and extended Kalman filtering SoC estimation technique is the most efficient and dynamically correct ECM model developed.