Abstract: Bio-battery represents an entirely new long term, reasonable, reachable and ecofriendly approach to production of sustainable energy. Types of batteries have been developed using MnO2 in various ways. MnO2 is suitable with physical, chemical, electrochemical, and catalytic properties, serving as an effective cathodic depolarizer and may be considered as being the life blood of the battery systems. In the present experimental work, we have studied the effect of generation of power by bio-battery using different concentrations of MnO2. The tests show that it is possible to generate electricity using cow’s urine as an electrolyte. After ascertaining the optimum concentration of MnO2, various battery parameters and performance indicates that cow urine solely produces power of 695 mW, while a combination with MnO2 (40%) enhances power of bio-battery, i.e. 1377 mW. On adding more and more MnO2 to the electrolyte, the power suppressed because inflation of internal resistance. The analysis of the data produced from experiment shows that MnO2 is quite suitable to energize the bio-battery.
Abstract: Recently, Electric Vehicles (EVs) have received extensive consideration since they offer a more sustainable and greener transportation alternative compared to fossil-fuel propelled vehicles. Lithium-Ion (Li-ion) batteries are increasingly being deployed in EVs because of their high energy density, high cell-level voltage, and low rate of self-discharge. Since Li-ion batteries represent the most expensive component in the EV powertrain, accurate monitoring and control strategies must be executed to ensure their prolonged lifespan. The Battery Management System (BMS) has to accurately estimate parameters such as the battery State-of-Charge (SOC), State-of-Health (SOH), and Remaining Useful Life (RUL). In order for the BMS to estimate these parameters, an accurate and control-oriented battery model has to work collaboratively with a robust state and parameter estimation strategy. Since battery physical parameters, such as the internal resistance and diffusion coefficient change depending on the battery state-of-life (SOL), the BMS has to be adaptive to accommodate for this change. In this paper, an extensive battery aging study has been conducted over 12-months period on 5.4 Ah, 3.7 V Lithium polymer cells. Instead of using fixed charging/discharging aging cycles at fixed C-rate, a set of real-world driving scenarios have been used to age the cells. The test has been interrupted every 5% capacity degradation by a set of reference performance tests to assess the battery degradation and track model parameters. As battery ages, the combined model parameters are optimized and tracked in an offline mode over the entire batteries lifespan. Based on the optimized model, a state and parameter estimation strategy based on the Extended Kalman Filter (EKF) and the relatively new Smooth Variable Structure Filter (SVSF) have been applied to estimate the SOC at various states of life.
Abstract: Two types of commercial cylindrical lithium ion
batteries (Panasonic 3.4 Ah NCR-18650B and Samsung 2.9 Ah
INR-18650), were investigated experimentally. The capacities of these
samples were individually measured using constant current-constant
voltage (CC-CV) method at different ambient temperatures (-10°C,
0°C, 25°C). Their internal resistance was determined by
electrochemical impedance spectroscopy (EIS) and pulse discharge
methods. The cells with different configurations of parallel connection
NCR-NCR, INR-INR and NCR-INR were charged/discharged at the
aforementioned ambient temperatures. The results showed that the
difference of internal resistance between cells much more evident at
low temperatures. Furthermore, the parallel connection of NCR-NCR
exhibits the most uniform temperature distribution in cells at -10°C,
this feature is quite favorable for the safety of the battery pack.