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In this paper, an energy management method is proposed for a power-split plug-in hybrid electric vehicle (PHEV). Through analyzing the PHEV powertrain, a series of quadratic equations are employed to approximate the vehicle's fuel-rate, using battery current as the input. Pontryagin's Minimum Principle (PMP) is introduced to find the battery current commands by solving the Hamiltonian function. Simulated Annealing (SA) algorithm is applied to calculate the engine-on power and the maximum current coefficient. Moreover, the battery state of health (SOH) is introduced to extend the application of the proposed algorithm. Simulation results verified that the proposed algorithm can reduce fuel-consumption compared to charge-depleting (CD) and charge-sustaining (CS) mode. [ABSTRACT FROM AUTHOR]

Abstract: State of health (SOH) of batteries in electric and hybrid vehicles can be observed using some battery parameters. Based on a resistance–capacitance circuit model of the battery and data obtained from abundant experiments, it was observed that the diffusion capacitance shows great correlation with SOH of a lithium-ion battery. However, accurate measurement of this diffusion capacitance in real time in an electric or hybrid electric vehicle is not practical. In this paper, Genetic Algorithm (GA) is employed to estimate the battery model parameters including the diffusion capacitance in real time using measurement of current and voltage of the battery. The battery SOH can then be determined using the identified diffusion capacitance. Temperature influence is also considered to improve the robustness and precision of SOH estimation results. Experimental results on various batteries further verified the proposed method. [Copyright &y& Elsevier]

Abstract: This paper deals with the problem of estimating the state of charge (SOC) of lithium-ion batteries. Based on the analysis of the impedance spectra obtained by electrochemical impedance spectroscopy (EIS), a simplified battery impedance model is derived with the constant phase element (CPE). To interpret the impedance model, a fractional order calculus (FOC) method is introduced to model the CPE in the impedance model. A new identification method is proposed to identify the impedance model, considering both accuracy and simplicity. The fractional Kalman filter is introduced to estimate the SOC of the lithium-ion battery based on the impedance model. Finally, a battery test bench is established, and the proposed method is verified by a scaled down system on the urban dynamometer driving schedule (UDDS) drive cycle test. [Copyright &y& Elsevier]

Wireless power transfer (WPT), which transmits power by an electromagnetic field across an intervening space, provides the prospect of new opportunities for electric vehicles (EVs) to enhance sustainable mobility. This review article evaluates WPT technology for EV applications from both technical and sustainability perspectives. The objectives of this review include: (1) to present the state-of-the-art technical progress and research bottlenecks in WPT development and applications in the transportation sector; (2) to characterize the demonstrations of the real-world deployment of WPT EV systems; and (3) to evaluate the sustainable performance and identify challenges and opportunities for improvement. From the technical perspective, progress on coil design, compensation topologies, and power electronics converters and control methods are reviewed with a focus on system performance. From the sustainability perspective, performance is defined in terms of energy, environmental, and economic metrics, and policy drivers and issues of health and safety are also examined. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a novel data-driven based approach for the estimation of the state of charge (SoC) of multiple types of lithium ion battery (LiB) cells with adaptive extended Kalman filter (AEKF). A modified second-order RC network based battery model is employed for the state estimation. Based on the battery model and experimental data, the SoC variation per mV voltage for different types of battery chemistry is analyzed and the parameters are identified. The AEKF algorithm is then employed to achieve accurate data-driven based SoC estimation, and the multi-parameter, closed loop feedback system is used to achieve robustness. The accuracy and convergence of the proposed approach is analyzed for different types of LiB cells, including convergence behavior of the model with a large initial SoC error. The results show that the proposed approach has good accuracy for different types of LiB cells, especially for C/LFP LiB cell that has a flat open circuit voltage (OCV) curve. The experimental results show good agreement with the estimation results with maximum error being less than 3%. [Copyright &y& Elsevier]

Wireless charging, as opposed to plug-in charging, is an alternative charging method for electric vehicles (EVs) with rechargeable batteries and can be applicable to EVs with fixed routes, such as transit buses. This study adds to the current research of EV wireless charging by utilizing the Life Cycle Assessment (LCA) to provide a comprehensive framework for comparing the life cycle energy demand and greenhouse gas emissions associated with a stationary wireless charging all-electric bus system to a plug-in charging all-electric bus system. Life cycle inventory analysis of both plug-in and wireless charging hardware was conducted, and battery downsizing, vehicle lightweighting and use-phase energy consumption were modeled. A bus system in Ann Arbor and Ypsilanti area in Michigan is used as the basis for bus system modeling. Results show that the wirelessly charged battery can be downsized to 27–44% of a plug-in charged battery. The associated reduction of 12–16% in bus weight for the wireless buses can induce a reduction of 5.4–7.0% in battery-to-wheel energy consumption. In the base case, the wireless charging system consumes 0.3% less energy and emits 0.5% less greenhouse gases than the plug-in charging system in the total life cycle. To further improve the energy and environmental performance of a wireless charging electric bus system, it is important to focus on key parameters including carbon intensity of the electric grid and wireless charging efficiency. If the wireless charging efficiency is improved to the same level as the assumed plug-in charging efficiency (90%), the difference of life cycle greenhouse gas emissions between the two systems can increase to 6.3%. [ABSTRACT FROM AUTHOR]

In this paper, an energy management strategy is proposed for a series plug-in hybrid electric vehicle. A number of quadratic equations are employed to determine the engine fuel-rate with respect to battery power. The problem is solved by using quadratic programming and simulated annealing method together to find the optimal battery power commands and the engine-on power. The influences induced by the inertias of the engine and generators are analyzed to improve the calculation precision. In addition, the state of health of the battery is taken into account to extend the application of the proposed method. Simulations were performed to verify that the proposed algorithm can decrease fuel consumption of plug-in hybrid electric vehicles. [ABSTRACT FROM AUTHOR]

There is a growing need to provide more realistic and accurate State of Health estimations for batteries in electric vehicles. Thus, it is necessary to research various lithium-ion cell aging processes, including cell degradation and related path dependence. This paper focuses on quantitative analyses of cell aging path dependence in a repeatable laboratory setting, considering the influence of duty cycles, depth of discharge (DOD), and the frequency and severity of the thermal cycle, as reflected in pure electric buses operated in Beijing. Incremental capacity analysis (ICA) and differential voltage analysis (DVA) are applied to infer cell degradation mechanisms and quantify the attributions to capacity fade. It was observed that the cells experienced a higher rate of aging at 80% DOD and an accelerated aging at 40 °C in the thermal cycling, as a result of possible loss of active material (LAM) in both electrodes, in addition to the loss of lithium inventory (LLI) and inhibited kinetics. The slight capacity fade from low-temperature extremes likely caused by LLI due to lithium plating, whereas the noticeable fade after the high-temperature excursion was likely caused by LAM and hindrance to kinetics. These results may lead to improved battery management in EV applications. [ABSTRACT FROM AUTHOR]