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Abstract This paper describes a new approach to estimate accurately the battery residual capacity (BRC) of the nickel–metal hydride (Ni–MH) battery for modern electric vehicles (EVs). The key to this approach is to model the Ni–MH battery in EVs by using the adaptive neuro-fuzzy inference system (ANFIS) with newly defined inputs and output. The inputs are the temperature and the discharged capacity distribution describing the discharge current profile, while the output is the state of available capacity (SOAC) representing the BRC. The estimated SOAC from ANFIS model and the measured SOAC from experiments are compared, and the results confirm that the proposed approach can provide an accurate estimation of the SOAC under variable discharge currents.

Based on the available energy sources, the electric vehicle (EV) cannot compete with the conventional vehicle in terms of driving range and initial cost. In the near future, the hybrid EV (HEV) is not only an interim solution for implementation of zero emission vehicles but a practical solution for commercialization of super-ultra-low-emission vehicles. This paper firstly presents an overview of latest HEVs, with emphasis on power management. Based on the power management strategy of the drive train, a new classification approach for HEVs is proposed. Hence, the corresponding system configurations are identified. The power flow control for various HEVs is also elaborated. Finally, the development trends of HEVs and EVs are delineated.

Abstract The ability to calculate the battery available capacity (BAC) for electric vehicles (EVs) is very important. Knowing the BAC and, thus, the driving range cannot only prevent EVs from being stranding on the road but also optimize the utilization of the battery energy storage in EVs. In order to determine the BAC, this paper presents a new neural network (NN) model of the lead–acid battery, based on the battery discharge current and temperature. Comparisons between the calculated BAC from the NN model and the measured BAC from experiments show good agreement. Furthermore, this new approach can readily be extended to the calculation of the BAC for other types of batteries.

The impetus of the internal combustion engine developments is the reductions of the fuel consumptions and exhaust emissions. Thermal management is an efficient method to decrease the exhaust emissions and enhance fuel economy. In order to further optimize the thermal management of internal combustion engines, a detailed analysis of the energy flow in each component of internal combustion engines is indispensable. In this paper, the test bench of a heavy duty diesel engine was established to obtain the target parameters. The energy distributions in each component of the diesel engine, including compressor, intercooler, shaft power, turbine, coolant and exhaust, were calculated using tested parameters. The lubricating oil consumption was also taken into consideration. In addition, the potential influences of different turbochargers on the total thermal efficiency were analyzed. The results showed that the thermal efficiency of the diesel engine was more than 38% when the engine operated at 50%–100% engine load and 1000 rpm–1700 rpm conditions. The energy loss by coolant was more than 50% of the total fuel energy consumption in the low power output conditions. However, it was lower than 30% in high power output conditions, and the thermal loss was more than 150 kW around rated power conditions. The maximum proportion of the energy being consumed by turbine was ∼10% of the fuel energy; additionally, the exhaust energy distributions changed significantly after the turbine expansion. 1%–3% of the fuel energy was recycled by the turbocharger, then, flowed into the cylinders. The energy loss through the intercooler accounted for ∼6% of the fuel energy. Significant reductions of exhaust emissions and fuel consumptions can be achieved by optimizing the coolant and lubricating oil thermal conditions. Turbochargers presented a huge effect on exhaust temperature distributions at high power output conditions, and the total thermal efficiency changed significantly if all kinds of energy recovery approaches were applied.

This paper describes a new adaptive neuro-fuzzy inference system (ANFIS) model to estimate accurately the battery residual capacity (BRC) of the lithium-ion (Li-ion) battery for modern electric vehicles (EVs). The key to this model is to adopt newly both the discharged/regenerative capacity distributions and the temperature distributions as the inputs and the state of available capacity (SOAC) as the output, which represents the BRC. Moreover, realistic EV discharge current profiles are newly used to formulate the proposed model. The accuracy of the estimated SOAC obtained from the model is verified by experiments under various EV discharge current profiles.

AbstractElectric Vehicle (EV) can save energy while reducing emissions and has thus attracted the attention of both academics and industry. The cost and benefit of charging are one of the key issues in relation to EV development that has been researched extensively. But many studies are carried out from a viewpoint of some local entities rather than a global system, focus on specific types or aspects of EV charging, or use mixed models that can only be computed by computer simulation and lack physical transparency. This paper illuminated that it is necessary to consider the value of EV charging on a system scale. In order to achieve this, it presents an analytical model for analyzing the overall value of EVs, an analysis model to evaluate the reduction of pollutions relevant to photovoltaic power, and a model to transfer the intrinsic savings of wind power to the off-peak charging loads. It is estimated that EV charging has a significant positive value, providing the basis for enhanced EV subsidies. Accordingly, a utilization mechanism apt to optimize globally is proposed, upon which sustainable business models can be formed by providing adequate support, including the implementation of a peak–valley tariff, charging subsidies and one-time battery subsidies. This utilization mechanism, by taking full advantage of the operation system of power utilities to provide basic support and service, may provide new approaches to the development of EVs. The method proposed here is of important value for the systematic considerations about EV development and maybe can help broaden the possibility of EV development.