Battery cell balance of electric vehicles under fast-DC charging

Electric vehicle (EV) range, recharge opportunities and time to recharge are major barriers to mainstream acceptance. Fast-DC charging has the potential to overcome these barriers. This research investigates the impact of fast-DC charging on battery cell balance, charge capacity and range for an EV travelling long distances on an 'electric-highway'. Two commercially available EVs were exposed to a series of discharge and fast-DC charge cycles to measure cell balance and charge capacity. The vehicles' battery management systems (BMS) were capable of successfully balancing individual cells and hence maintaining the batteries' charge capacity. Although fast-DC charge levels and discharge safety margins significantly reduced the vehicles' charge capacity and range as stated by the manufacturer, these values remained stable for the test period. In regards to cell balance and charge capacity, our research suggests that fast-DC charging technology is a feasible option for EVs to travel large distances in a day

Dynamics of Simple Balancing Models with State Dependent Switching Control

Time-delayed control in a balancing problem may be a nonsmooth function for a variety of reasons. In this paper we study a simple model of the control of an inverted pendulum by either a connected movable cart or an applied torque for which the control is turned off when the pendulum is located within certain regions of phase space. Without applying a small angle approximation for deviations about the vertical position, we see structurally stable periodic orbits which may be attracting or repelling. Due to the nonsmooth nature of the control, these periodic orbits are born in various discontinuity-induced bifurcations. Also we show that a coincidence of switching events can produce complicated periodic and aperiodic solutions.Comment: 36 pages, 12 figure

Automatically Designing Robot Controllers and Sensor Morphology with Genetic Programming

International audienceGenetic programming provides an automated design strategy to evolve complex controllers based on evolution in nature. In this contribution we use genetic programming to automatically evolve efficient robot controllers for a corridor following task. Based on tests executed in a simulation environment we show that very robust and efficient controllers can be obtained. Also, we stress that it is important to provide sufficiently diverse fitness cases, offering a sound basis for learning more complex behaviour. The evolved controller is successfully applied to real environments as well. Finally, controller and sensor morphology are co-evolved, clearly resulting in an improved sensor configuration

Limitations of testing standards for battery electric vehicles: accessories, energy usage, and range

Issues with hydrocarbon fuel supply security, price volatility, alongside environmental, and health concerns of conventional internal combustion engine (ICE) transport technologies have raised interest in electric vehicle (EV) alternatives. However, the methods of EV testing for performance and range are inadequate adaptations from ICE testing standards designed to measure liquid fuel economy and emissions under largely unrealistic conditions. This research assesses the performance of a battery EV (BEV) by conducting a number of real-world driving tests under varying conditions including the impact of vehicle accessory usage (lights, air-conditioning, stereo, heater etc.) and additional passengers. The authors’ results demonstrate that large increases of energy consumption from accessory usage and additional passengers do occur in BEVs, which remain outside of most published EV/BEV and ICE vehicle standard test results, which themselves have recently come under scrutiny for other reasons. Owing to the relatively small battery in modern BEVs, this additional loss in efficiency and range under real-world on-road conditions may severely compromise the nascent BEV industry; particularly in areas with limited charging infrastructure

Driving electric vehicles at highway speeds: The effect of higher driving speeds on energy consumption and driving range for electric vehicles in Australia

Electric vehicles (EVs) have the potential to operate emission free and thus overcome many environmental and health issues associated with cars run on fossil fuels. Recharging time and driving range are amongst the biggest hurdles for the mainstream acceptance and implementation of EV technology. Fast-DC charging significantly reduces the recharging time and can be used to make longer EV trips possible, e.g. on highways between cities. Although some EV and hybrid car studies have been conducted that address separately issues such as limited drivable ranges, charge stations, impact from auxiliary loads on vehicle energy consumption and emissions, there is currently limited research on the impact on drivable range from the combination of driving EVs at highway speeds, using auxiliary loads such as heating or air conditioning (AC), and reduced charge capacity from fast-DC charging and discharge safety margins. In this study we investigate these parameters and their impact on energy consumption and drivable range of EVs. Our results show a significantly reduced range under conditions relevant for highway driving and significant deviation from driving ranges published by EV manufacturers. The results and outcomes of this project are critical for the efficient design and implementation of so-called ‘Electric Highways’. To prevent stranded cars and a possible negative perception of EVs, drivers and charging infrastructure planners need be aware of how EV energy and recharging demands can significantly change under different loads and driving patterns

Testing energy efficiency and driving range of electric vehicles in relation to gear selection

Electric vehicles (EVs) have the potential to be operated using a clean, renewable energy source. However, a major limitation is their relatively short vehicle driving range and the associated driver ‘range anxiety’. This research investigates the effect of gearing on energy consumption and driving range efficiency on an EV-converted Ford Focus using a chassis dynamometer in a controlled test environment in accordance with international standards. Two designs of the Ford Focus were used in the tests; one with an automatic gear drive, and the other with a manual gear drive. The electricity consumption of the two cars driving under different gearing configurations was measured under identical drive cycles. The vehicle range tests showed that measuring energy consumption on just two consecutive drive cycles on a calibrated chassis dynamometer will lead to a small overestimation of the energy consumption due to a ‘cold’ drive train. The results also suggest greater attention needs to be paid to EV battery charger efficiency, particularly in terms of standby energy consumption, which can increase the total energy required for EV owners markedly

Enhanced EV and ICE vehicle energy efficiency through drive cycle synchronisation of deferred auxiliary loads

This research investigates energy efficiency improvements by synchronising auxiliary air-conditioning (AC) with the vehicle drive train on a real road driving cycle pattern. The research findings are applicable to electric vehicles (EV), internal combustion engine (ICE) vehicles, and hybrids. An EV-converted Ford Focus was configured to operate the AC compressor solely from kinetic energy recovered from the drive train when coasting or slowing down. Test drives with the Ford Focus with standard AC operation increased the energy consumption by 11.6% compared with AC off, yet when the vehicle was synchronised with the drive train the energy consumption increased by only 5.8% compared with AC off, an energy saving of 8.1 Wh km-1. The configuration maintained comfortable cabin conditions (temperature and humidity) similar to driving with a standard AC system configuration. In vehicles with an interconnected automatic AC and engine management system data-bus, this efficiency improvement may require a software update only

Performance evaluation of regenerative braking system

This research evaluates the energy gain from a regenerative braking system (RBS) in a commercial electric vehicle (EV), the OEM Mitsubishi i-MiEV. Measurements were conducted in a controlled environment on a commercial chassis dynamometer using international drive cycle standards. The energy recovery of the vehicle was modelled and the output of the model was compared with results from the chassis dynamometer driving. The experiments were original as they coupled changes in energy recovered and driving range due to the RBS settings with investigations into the time of use of the friction brake. Performance tests used two different drive cycle speed profiles and various RBS settings to compare energy recovery performance for a broad range of driving styles. The results show that due to reduced energy consumption, the RBS increased the driving range by 11–22% depending on RBS settings and the drive cycle settings on the dynamometer. The results further showed that driving an EV with a RBS uses the friction brakes more efficiently, which will reduce brake pad wear. This has the potential to improve air quality due to reduced brake pad dust and reduces the maintenance costs of the vehicle. The findings were significant since they showed that friction time of use, a parameter neglected in RBS testing, plays an important part in the efficient operation of an EV. The overall results from the vehicle energy recovery modelling showed good agreement with the data from drive cycle testing and the model has potential to be further developed to gain greater insight into vehicle RBS braking behaviour for EVs in general

Smart accelerating and braking achieving higher energy efficiencies in electric vehicles

Efficient operation of EVs is critical to optimise the usage of their relative small energy storage. Although each motor controller has a unique range of motor rotational speeds (rpm) and loads for optimal efficiency, EVs lack variable gearboxes that can match vehicle speed and motor rpm to efficient controller regions. EVs thus rely on the driver to actively influence the load by changing acceleration or deceleration rates for a more efficient operation. Despite this, most EV efficiency studies use speed profiles with small changes in acceleration and deceleration rates. This study investigates the impact of various high load variations in accelerations and decelerations on energy consumption. The results show significant improvements in efficiency and reduced energy consumption by applying high loads at low vehicle speeds and strong deceleration rates. However, the increased losses under certain high acceleration rates outweighed the benefits of loading an EV