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With the continuing transition to renewable inherently intermittent energy sources like solar- and wind power, electrical energy storage will become progressively more important to manage energy production and demand. A key technology in this area is Li-ion batteries. To operate these batteries efficiently, there is a need for monitoring of the cur...
Citations
... [65] The lithium-ion battery market has historically been dominated by NMC and NCA chemistries. [66][67][68] Earlier predictions anticipated that NMC and NCA would continue to dominate the market by 2030. [32,69,70] However, more recent predictions suggest a shift in the landscape, with LFP chemistry gaining greater prominence compared to previous forecasts, reaching up to 47 % by 2026. ...
Cost‐savings in lithium‐ion battery production are crucial for promoting widespread adoption of Battery Electric Vehicles and achieving cost‐parity with internal combustion engines. This study presents a comprehensive analysis of projected production costs for lithium‐ion batteries by 2030, focusing on essential metals. It explores the complex interplay of factors, including economies of scale, R&D innovations, market dynamics, and metal price trends. The findings highlight the significant role of R&D innovations and economies of scale in substantial cost reductions by 2030, with projected ranges of 21–28 % and 25–37 %, respectively. However, potential cost escalations due to elevated metal prices, particularly for nickel‐cobalt‐containing chemistries, are also cautioned. To address these challenges, the study proposes a strategic shift towards robust Lithium‐Iron‐Phosphate (LFP) chemistry to mitigate cost pressures and meet predefined cost targets. Moreover, by analyzing medium or low metal price trends, the study reveals the potential for significant cost savings, with exceptional scenarios demonstrating up to a remarkable 65 % reduction in costs. Given the broad range of cost likelihoods, the study underscores the importance of vertical integration and international cooperation in managing the essential metals supply chain securely. The research has profound implications for policymakers and industry decision‐makers, providing valuable insights into the lithium‐ion battery industry.
... Therefore, as compared to battery charge balancing, the management problem becomes more complex for batteries of different ages. In practice, battery cells with less than 80% of their rated capacity are considered to no longer suit EV applications [20], but may still keep a huge value for stationary energy storage where operating conditions are more gentle and requirements on energy density are less strict [3,21]. With the intrinsic merit to balance batteries, RBSs cannot only prolong the first-life usage but also become imperatively important for second-life applications as shown in Fig. 1 (d). ...
div>Batteries are widely applied to the energy storage and power supply in portable electronics, transportation,
power systems, communication networks, etc. They are particularly demanded in the emerging
technologies of vehicle electrification and renewable energy integration for a green and sustainable society.
To meet various voltage, power, and energy requirements in large-scale applications, multiple battery
cells have to be connected in series and/or parallel. While battery technology has advanced significantly
in the past decade, existing battery management systems (BMSs) mainly focus on state monitoring and
control of battery systems packed in fixed configurations. In fixed configurations, though, the battery
system performance is in principle limited by the weakest cells, which can leave large parts severely
underutilized. Allowing dynamic reconfiguration of battery cells, on the other hand, allows individual and
flexible manipulation of the battery system at cell, module, and pack levels, which may open up a new
paradigm for battery management. Following this trend, this paper provides an overview of next-generation
BMSs featuring dynamic reconfiguration. Motivated by numerous potential benefits of reconfigurable
battery systems (RBSs), the hardware designs, management principles, and optimization algorithms for
RBSs are sequentially and systematically discussed. Theoretical and practical challenges during the design
and implementation of RBSs are highlighted in the end to stimulate future research and development.</div
... Therefore, as compared to battery charge balancing, the management problem becomes more complex for batteries of different ages. In practice, battery cells with less than 80% of their rated capacity are considered to no longer suit EV applications [20], but may still keep a huge value for stationary energy storage where operating conditions are more gentle and requirements on energy density are less strict [3,21]. With the intrinsic merit to balance batteries, RBSs cannot only prolong the first-life usage but also become imperatively important for second-life applications as shown in Fig. 1 (d). ...
div>Batteries are widely applied to the energy storage and power supply in portable electronics, transportation, power systems, communication networks, etc. They are particularly demanded in the emerging technologies of vehicle electrification and renewable energy integration for a green and sustainable society. To meet various voltage, power, and energy requirements in large-scale applications, multiple battery cells have to be connected in series and/or parallel. While battery technology has advanced significantly in the past decade, existing battery management systems (BMSs) mainly focus on state monitoring and control of battery systems packed in fixed configurations. In fixed configurations, though, the battery system performance is in principle limited by the weakest cells, which can leave large parts severely underutilized. Allowing dynamic reconfiguration of battery cells, on the other hand, allows individual and flexible manipulation of the battery system at cell, module, and pack levels, which may open up a new paradigm for battery management. Following this trend, this paper provides an overview of next-generation BMSs featuring dynamic reconfiguration. Motivated by numerous potential benefits of reconfigurable battery systems (RBSs), the hardware designs, management principles, and optimization algorithms for RBSs are sequentially and systematically discussed. Theoretical and practical challenges during the design and implementation of RBSs are highlighted in the end to stimulate future research and development.</div
... Therefore, as compared to battery charge balancing, the management problem becomes more complex for batteries of different ages. In practice, battery cells with less than 80% of their rated capacity are considered to no longer suit electric vehicle (EV) applications [20], but they may still have value as stationary energy storage, where operating conditions are gentler and the energy density requirements are less strict [3], [21]. With the intrinsic merit of balancing batteries, RBSs can not only prolong first-life usage, but they also become imperatively important for second-life applications, as shown in Figure 1(d). ...
div>Batteries are widely applied to the energy storage and power supply in portable electronics, transportation,
power systems, communication networks, etc. They are particularly demanded in the emerging
technologies of vehicle electrification and renewable energy integration for a green and sustainable society.
To meet various voltage, power, and energy requirements in large-scale applications, multiple battery
cells have to be connected in series and/or parallel. While battery technology has advanced significantly
in the past decade, existing battery management systems (BMSs) mainly focus on state monitoring and
control of battery systems packed in fixed configurations. In fixed configurations, though, the battery
system performance is in principle limited by the weakest cells, which can leave large parts severely
underutilized. Allowing dynamic reconfiguration of battery cells, on the other hand, allows individual and
flexible manipulation of the battery system at cell, module, and pack levels, which may open up a new
paradigm for battery management. Following this trend, this paper provides an overview of next-generation
BMSs featuring dynamic reconfiguration. Motivated by numerous potential benefits of reconfigurable
battery systems (RBSs), the hardware designs, management principles, and optimization algorithms for
RBSs are sequentially and systematically discussed. Theoretical and practical challenges during the design
and implementation of RBSs are highlighted in the end to stimulate future research and development.</div
... Therefore, as compared to battery charge balancing, the management problem becomes more complex for batteries of different ages. In practice, battery cells with less than 80% of their rated capacity are considered to no longer suit electric vehicle (EV) applications [20], but they may still have value as stationary energy storage, where operating conditions are gentler and the energy density requirements are less strict [3], [21]. With the intrinsic merit of balancing batteries, RBSs can not only prolong first-life usage, but they also become imperatively important for second-life applications, as shown in Figure 1(d). ...
div>Batteries are widely applied to the energy storage and power supply in portable electronics, transportation, power systems, communication networks, etc. They are particularly demanded in the emerging technologies of vehicle electrification and renewable energy integration for a green and sustainable society. To meet various voltage, power, and energy requirements in large-scale applications, multiple battery cells have to be connected in series and/or parallel. While battery technology has advanced significantly in the past decade, existing battery management systems (BMSs) mainly focus on state monitoring and control of battery systems packed in fixed configurations. In fixed configurations, though, the battery system performance is in principle limited by the weakest cells, which can leave large parts severely underutilized. Allowing dynamic reconfiguration of battery cells, on the other hand, allows individual and flexible manipulation of the battery system at cell, module, and pack levels, which may open up a new paradigm for battery management. Following this trend, this paper provides an overview of next-generation BMSs featuring dynamic reconfiguration. Motivated by numerous potential benefits of reconfigurable battery systems (RBSs), the hardware designs, management principles, and optimization algorithms for RBSs are sequentially and systematically discussed. Theoretical and practical challenges during the design and implementation of RBSs are highlighted in the end to stimulate future research and development.</div
... En effet, la plupart des études de vieillissement calendaire des batteries lithium-ion de la littérature ( [7], [52], [144]- [146]) montrent que l'évolution de la perte de capacité des batteries (QF) peut être modélisée par l'équation suivante : Nous avons dans un premier temps étudié seulement l'influence de la température en négligeant celle du SoC. En fait, la plupart des usages enregistrés sont réalisés à un SoC élevé. ...
Currently, most battery life predictions in electromobility applications are based on models and results from standardized cycling tests. The profiles used for this accelerated aging are made from charges / discharges, partial or total, at constant currents, or formed by simplified profiles inspired by real uses. They include simple impulses and only partially represent the diversity of uses.
The specificity of this thesis lies in the fact that the battery aging study is based on data from several electric vehicles in real-life application from the CROME project.
In the first part of this manuscript, we analyzed and classified the different registered uses, which allowed us to identify 5 modes of use that represent all the registered uses. These modes of use include 3 types of driving and 2 types of recharging.
We then studied the influence of each of these modes of use on aging using different methods and validated the various obtained results by performing several experimental tests of calendar and cycling aging.
Finally, we built several usage scenarios and studied their respective aging. We have, on the one hand, experimentally compared the aging produced by a representative profile of real uses with that generated by the WLTC standardized profile. On the other hand, we studied, based on our simulator, the influence of the frequency and periodicity of recharging on aging as well as the impact of V2H (Vehicle to Home) use on battery life.
... Initial parameters in the proposed IAEKF algorithm are needed for SOC estimation. Table 3 listed initial parameters [64]. ...
Adaptive extended Kalman filter (AEKF) is widely used for lithium-ion battery (LIBs) state of charge (SOC) estimation. Innovation covariance matrix (ICM) of AEKF is estimated by fixed-length error innovation sequence (EIS) (the difference between measured and estimated voltages), which doesn't consider the distribution change of EIS. However, the distribution of EIS will change due to load current dynamics or error of battery model. Failing to consider the distribution change of EIS will lead to SOC estimation inaccuracy. To address this problem, this paper proposed an intelligent adaptive extended Kalman filter (IAEKF) method that can detect the moment of distribution change of EIS by the maximum likelihood function. Then, the ICM is updated based on the EIS after that moment to improve the SOC estimation accuracy. Results show that the proposed IAEKF method improves SOC estimation accuracy. Compared to that of the AEKF, the Root Mean Squared Error (RMSE) and the Mean Absolute Error (MAE) of SOC based on IAEKF decrease significantly by 43.34% and 55.80%, respectively, while the computation time only increases by 4.59%. In the end, the effect of initial parameters on the SOC estimation accuracy was analysed. It is found that the proposed IAEKF method is robust against parameter uncertainties.
Adaptive unscented Kalman filter (AUKF) has been widely used for state of charge (SOC) estimation of lithium‐ion battery. The noise covariance of the conventional AUKF method is updated based on the innovation covariance matrix (ICM), which is estimated using the error innovation sequence (EIS). However, the distribution of EIS changes due to the time‐varying noise, load current dynamics and modelling error, which will lead to inaccurate ICM estimation. Therefore, an intelligent adaptive unscented Kalman filter (IAUKF) method is proposed to detect the distribution change of EIS. Then, the ICM is estimated based on the EIS after the distribution change. Results show that the IAUKF method can improve SOC estimation accuracy significantly. Compared with that of the AUKF method, the root mean squared error and the mean absolute error of SOC based on the IAUKF method decrease by 43.70% and 72.37% under random walk discharge condition, respectively. In addition, the computation time of the IAUKF method slightly increases by 6.27% compared with that of AUKF method. Finally, the effect of initial parameters on the SOC estimation accuracy was analysed. The results indicate that proper algorithm tuning, such as initial window length of EIS for ICM update and the threshold value, can further improve the SOC accuracy based on the proposed IAUKF method. The proposed IAUKF method also shows high robustness against initial measurement noise covariance. An intelligent adaptive unscented Kalman filter (IAUKF) method is proposed to detect the distribution change of error innovation sequence (EIS) to improve the SOC estimation accuracy. Compared to that of the AUKF method, the Root Mean Squared Error (RMSE) and the Mean Absolute Error (MAE) of SOC based on the IAUKF method decrease by 43.70% and 72.37% under random walk discharge condition.
