Closed-loop optimization of fast-charging protocols for batteries with machine learning.

Simultaneously optimizing many design parameters in time-consuming experiments causes bottlenecks in a broad range of scientific and engineering disciplines1,2. One such example is process and control optimization for lithium-ion batteries during materials selection, cell manufacturing and operation. A typical objective is to maximize battery lifetime; however, conducting even a single experiment to evaluate lifetime can take months to years3-5. Furthermore, both large parameter spaces and high sampling variability3,6,7 necessitate a large number of experiments. Hence, the key challenge is to reduce both the number and the duration of the experiments required. Here we develop and demonstrate a machine learning methodology  to efficiently optimize a parameter space specifying the current and voltage profiles of six-step, ten-minute fast-charging protocols for maximizing battery cycle life, which can alleviate range anxiety for electric-vehicle users8,9. We combine two key elements to reduce the optimization cost: an early-prediction model5, which reduces the time per experiment by predicting the final cycle life using data from the first few cycles, and a Bayesian optimization algorithm10,11, which reduces the number of experiments by balancing exploration and exploitation to efficiently probe the parameter space of charging protocols. Using this methodology, we rapidly identify high-cycle-life charging protocols among 224 candidates in 16 days (compared with over 500 days using exhaustive search without early prediction), and subsequently validate the accuracy and efficiency of our optimization approach. Our closed-loop methodology automatically incorporates feedback from past experiments to inform future decisions and can be generalized to other applications in battery design and, more broadly, other scientific domains that involve time-intensive experiments and multi-dimensional design spaces

Peak shaving through battery storage for low-voltage enterprises with peak demand pricing

The renewable energy transition has introduced new electricity tariff structures. With the increased penetration of photovoltaic and wind power systems, users are being charged more for their peak demand. Consequently, peak shaving has gained attention in recent years. In this paper, we investigated the potential of peak shaving through battery storage. The analyzed system comprises a battery, a load and the grid but no renewable energy sources. The study is based on 40 load profiles of low-voltage users, located in Belgium, for the period 1 January 2014, 00:00-31 December 2016, 23:45, at 15 min resolution, with peak demand pricing. For each user, we studied the peak load reduction achievable by batteries of varying energy capacities (kWh), ranging from 0.1 to 10 times the mean power (kW). The results show that for 75% of the users, the peak reduction stays below 44% when the battery capacity is 10 times the mean power. Furthermore, for 75% of the users the battery remains idle for at least 80% of the time; consequently, the battery could possibly provide other services as well if the peak occurrence is sufficiently predictable. From an economic perspective, peak shaving looks interesting for capacity invoiced end users in Belgium, under the current battery capex and electricity prices (without Time-of-Use (ToU) dependency)

Prospects of electric vehicles in the developing countries : a literature review

Electric mobility offers a low cost of travel along with energy and harmful emissions savings. Nevertheless, a comprehensive literature review is missing for the prospects of electric vehicles in developing countries. Such an overview would be instrumental for policymakers to understand the barriers and opportunities related to different types of electric vehicles (EVs). Considering the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines, a systematic review was performed of the electronic databases Google Scholar and Web of Science for the years 2010–2020. The electric four-wheelers, hybrid electric vehicles and electric two-wheeler constituted the electric vehicles searched in the databases. Initially, 35 studies identified in the Web of Science that matched the criteria were studied. Later, 105 other relevant reports and articles related to barriers and opportunities were found by using Google Scholar and studied. Results reveal that electric four-wheelers are not a feasible option in developing countries due to their high purchase price. On the contrary, electric two-wheelers may be beneficial as they come with a lower purchase price

Development Schemes of Electric Vehicle Charging Protocols and Implementation of Algorithms for Fast Charging under Dynamic Environments Leading towards Grid-to-Vehicle Integration

This thesis focuses on the development of electric vehicle (EV) charging protocols under a dynamic environment using artificial intelligence (AI), to achieve Vehicle-to-Grid (V2G) integration and promote automobile electrification. The proposed framework comprises three major complementary steps. Firstly, the DC fast charging scheme is developed under different ambient conditions such as temperature and relative humidity. Subsequently, the transient performance of the controller is improved while implementing the proposed DC fast charging scheme. Finally, various novel techno-economic scenarios and case studies are proposed to integrate EVs with the utility grid. The proposed novel scheme is composed of hierarchical stages; In the first stage, an investigation of the temperature or/and relative humidity impact on the charging process is implemented using the constant current-constant voltage (CC-CV) protocol. Where the relative humidity impact on the charging process was not investigated or mentioned in the literature survey. This was followed by the feedforward backpropagation neural network (FFBP-NN) classification algorithm supported by the statistical analysis of an instant charging current sample of only 10 seconds at any ambient condition. Then the FFBP-NN perfectly estimated the EV’s battery terminal voltage, charging current, and charging interval time with an error of 1% at the corresponding temperature and relative humidity. Then, a nonlinear identification model of the lithium-polymer ion battery dynamic behaviour is introduced based on the Hammerstein-Wiener (HW) model with an experimental error of 1.1876%. Compared with the CC-CV fast charging protocol, intelligent novel techniques based on the multistage charging current protocol (MSCC) are proposed using the Cuckoo optimization algorithm (COA). COA is applied to the Hierarchical technique (HT) and the Conditional random technique (CRT). Compared with the CC-CV charging protocol, an improvement in the charging efficiency of 8% and 14.1% was obtained by the HT and the CRT, respectively, in addition to a reduction in energy losses of 7.783% and 10.408% and a reduction in charging interval time of 18.1% and 22.45%, respectively. The stated charging protocols have been implemented throughout a smart charger. The charger comprises a DC-DC buck converter controlled by an artificial neural network predictive controller (NNPC), trained and supported by the long short-term memory neural network (LSTM). The LSTM network model was utilized in the offline forecasting of the PV output power, which was fed to the NNPC as the training data. The NNPC–LSTM controller was compared with the fuzzy logic (FL) and the conventional PID controllers and perfectly ensured that the optimum transient performance with a minimum battery terminal voltage ripple reached 1 mV with a very high-speed response of 1 ms in reaching the predetermined charging current stages. Finally, to alleviate the power demand pressure of the proposed EV charging framework on the utility grid, a novel smart techno-economic operation of an electric vehicle charging station (EVCS) in Egypt controlled by the aggregator is suggested based on a hierarchical model of multiple scenarios. The deterministic charging scheduling of the EVs is the upper stage of the model to balance the generated and consumed power of the station. Mixed-integer linear programming (MILP) is used to solve the first stage, where the EV charging peak demand value is reduced by 3.31% (4.5 kW). The second challenging stage is to maximize the EVCS profit whilst minimizing the EV charging tariff. In this stage, MILP and Markov Decision Process Reinforcement Learning (MDP-RL) resulted in an increase in EVCS revenue by 28.88% and 20.10%, respectively. Furthermore, the grid-to-vehicle (G2V) and vehicle-to-grid (V2G) technologies are applied to the stochastic EV parking across the day, controlled by the aggregator to alleviate the utility grid load demand. The aggregator determined the number of EVs that would participate in the electric power trade and sets the charging/discharging capacity level for each EV. The proposed model minimized the battery degradation cost while maximizing the revenue of the EV owner and minimizing the utility grid load demand based on the genetic algorithm (GA). The implemented procedure reduced the degradation cost by an average of 40.9256%, increased the EV SOC by 27%, and ensured an effective grid stabilization service by shaving the load demand to reach a predetermined grid average power across the day where the grid load demand decreased by 26.5% (371 kW)

Towards more realistic Li-ion battery safety tests based on Li-plating as internal cell error

Herein the development of a novel test procedure for lithium ion cells to trigger cell internal defects under more realistic conditions (ambient temperature/voltage) than procedures that are currently used as triggers for propagation tests (e.g. nail penetration, massive heating/overcharge) is reported. Therefore, lithium plating, a common phenomenon of aging but also appearance of misbalanced/inhomogeneous cell construction, occurring within graphite-based lithium-ion battery cells, was chosen to be induced systematically and reproducibly by electrochemical cycling within different lithium-ion cell chemistries as well as pouch cell types (commercial and self-made). Using plating as a trigger method on different kind of test cells, showed that the behavior of cells differed, such that it is possible to classify the cells into categories based on the outcome of the test (e.g., swelling, venting, thermal runaway). Interestingly, not the lithium plating and dendrite growth through the separator itself but its consequences, such as degradation reactions with increase in inner resistance caused safety critical behavior of the cells. The test procedure can be applied on an electrically connected cell within a battery system and therefore has great potential to be used alternatively as a trigger method for the propagation test

Energy Storage Systems for Traction and Renewable Energy Applications

Energy storage systems are the set of technologies used to store various forms of energy, and by necessity, can be discharged. Energy storage technologies have a wide range of characteristics and specifications. Like any other technology, each type of energy storage has its pros and cons. Depending on the application, it is crucial to perform a tradeoff study between the various energy storage options to choose the optimal solution based on the key performance objectives and various aspects of those technologies. The purpose of this thesis is to present a thorough literature review of the various energy storage options highlighting the key tradeoffs involved. This thesis focuses on evaluating energy storage options for traction and renewable energy applicationsHybrid Electric Vehicles (HEVs) is one key application space driving breakthroughs in energy storage technologies. The focus though has been typically on using one type of energy storage systems. This thesis investigates the impact of combining several types of batteries with ultracapacitor. A case study of integrating two energy storage systems in a series-parallel hybrid electric vehicle is simulated by using MATLAB-SIMULINK software.The other key application space is renewable energy especially wind and solar. Due to the intermittent nature of renewable energy sources, energy storage is a must to achieve the required power quality. Therefore, this thesis aims to investigate different cases of combining different types of energy storage with wind and solar. Hybrid Optimization Model for Electric Renewables (HOMER) software is utilized to study the economic and sizing aspects in each case

Challenges and Opportunities for Second-life Batteries: A Review of Key Technologies and Economy

Due to the increasing volume of Electric Vehicles in automotive markets and the limited lifetime of onboard lithium-ion batteries (LIBs), the large-scale retirement of LIBs is imminent. The battery packs retired from Electric Vehicles still own 70%-80% of the initial capacity, thus having the potential to be utilized in scenarios with lower energy and power requirements to maximize the value of LIBs. However, spent batteries are commonly less reliable than fresh batteries due to their degraded performance, thereby necessitating a comprehensive assessment from safety and economic perspectives before further utilization. To this end, this paper reviews the key technological and economic aspects of second-life batteries (SLBs). Firstly, we introduce various degradation models for first-life batteries and identify an opportunity to combine physics-based theories with data-driven methods to establish explainable models with physical laws that can be generalized. However, degradation models specifically tailored to SLBs are currently absent. Therefore, we analyze the applicability of existing battery degradation models developed for first-life batteries in SLB applications. Secondly, we investigate fast screening and regrouping techniques and discuss the regrouping standards for the first time to guide the classification procedure and enhance the performance and safety of SLBs. Thirdly, we scrutinize the economic analysis of SLBs and summarize the potentially profitable applications. Finally, we comprehensively examine and compare power electronics technologies that can substantially improve the performance of SLBs, including high-efficiency energy transformation technologies, active equalization technologies, and technologies to improve reliability and safety

Advanced thermal management system driven by phase change materials for power lithium-ion batteries: A review

Power lithium-ion batteries are widely utilized in electric vehicles (EVs) and hybrid electric vehicles (HEVs) for their high energy densities and long service-life. However, thermal safety problems mainly resulting from thermal runaway (TR) must be solved. In general, temperature directly influences the performance of lithium-ion batteries. Hence, an efficient thermal management system is very necessary for battery modules/packs. One particular approach, phase change material (PCM)-based cooling, has exhibited promising applicability due to prominent controlling-temperature and stretching-temperature capacities. However, poor thermal conductivity performance, as the main technical bottleneck, is limiting the practical application. Nevertheless, only promoting the thermal conductivity is far from enough considering the practical application in EVs/HEVs. To fix these flaws, firstly, the heat generation/transfer mechanisms of lithium-ion power batteries were macro- and microscopically reviewed. Following that, the thermal conductivity, structural stability, and flame retardancy of PCM are thoroughly discussed, to which solutions to the aforementioned performances are systematically reviewed. In addition, battery thermal management system (BTMS) employing PCM is illustrated and compared. Eventually, the existing challenges and future directions of PCM-based BTMS are discussed. In summary, this review presents effective approaches to upgrade the PCM performances for high-density lithium-ion BTMS. These strategies furtherly accelerate the commercialization process of PCM BTMS