The study presented in this paper discusses developments in the area of anti-lock braking control for full electric vehicles. The main contributions of the paper are the development and experimental validation of the combined electric and hydraulic brake system with application of a continuous anti-lock braking system, which is expected to be more effective than the existing industrial solutions. It covers the topic of high-performance braking and driving comfort under a direct slip control function. The research is related to the full electric sport utility vehicle equipped with four individual on-board motors and a decoupled electrohydraulic brake system. The brake controller architecture includes functions of the continuous anti-lock braking system strategy, a brake blending algorithm aimed at minimization of the friction brake torque and operational limitations of the electric brakes. The developed brake controller was subjected to different validation procedures but, within the framework of this paper, emergency braking tests on a wet surface with a low coefficient of friction are considered. The results obtained demonstrate significant improvements in the braking performance, the driving comfort and the control performance for continuous anti-lock braking control of the electric vehicle compared with those of diverse vehicle configurations and, in particular, with those of a sport utility vehicle of the same type equipped with an internal-combustion engine and a conventional hydraulic brake system.

This paper describes a novel pendulum decay test for determining the transmission efficiency of chain drives. The test involves releasing a pendulum with an initial potential energy and measuring its decaying oscillations, under controlled conditions the decay reveals the losses in the transmission to a high degree of accuracy. The main advantage over motorised rigs is that there are significantly fewer sources of friction and inertia and hence measurement error. The pendulum rigs have an accuracy around 0.6% for the measurement of coefficient of friction, giving an accuracy of transmission efficiency measurement around 0.012%. A theoretical model of chain friction combined with the equations of motion enables coefficient of friction to be determined from the decay rate of pendulum velocity. The pendulum rigs operate at relatively low speeds. However, they allow an accurate determination of the coefficient of friction to estimate transmission efficiency at higher speeds. The pendulum rig revealed a previously undetected rocking behaviour in the chain links at very small articulation angles. In this regime, the link interfaces were observed to roll against one another rather than slide. This observation indicates that a very high efficiency transmission can be achieved if the articulation angle is very low.

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We present a method for the estimation of vehicle mass and road gradient for a light passenger vehicle. The estimation method uses information normally available on the vehicle CAN bus without the addition of extra sensors. A composite parameter estimation algorithm incorporating a nonlinear adaptive observer structure uses vehicle speed over ground and driving torque to estimate mass and road gradient. A system of filters is used to avoid deriving acceleration directly from wheel speed. In addition, a novel data fusion method makes use of the regressor structure to introduce information from other sensors in the vehicle. The dynamics of the additional sensors must be able to be parameterised using the same parameterisation as the complete vehicle system dynamics. In this case we make use of an Inertial Measurement Unit (IMU) which is part of the vehicle safety and Advanced Driver Assist Systems (ADAS). Therefore, a method using some filtering and supervisory logic is employed to give a sensible update behaviour for the vehicle mass estimation algorithm. The main function of the supervisor is to reject the mass estimate produced by unsuitable available data due to unmodelled loss forces. Good estimation results are obtained from data from a vehicle which was also fitted with some additional instrumentation including GPS sensors and a high quality IMU for scientific verification purposes.

This paper demonstrates a real world case study of a new method for robust simultaneous estimation of two parameters using multiple data sources. The method is used to simultaneously estimate vehicle mass and road gradient. No additional sensors are required beyond those that would normally be found on a vehicle controller network. The estimation algorithm combines components driven by observer state error and also directly by the parameter error using a sliding-mode inspired regressor structure. The algorithm incorporates a novel information fusion method that is integral to the regressor structure and a supervised data-rejection system to limit estimation activity in periods of recognised error-promoting activity. The estimation method has been demonstrated in real time on a modified production passenger car platform. It has been shown to be effective at robustly predicting road gradient and offering more reliable and stable prediction of vehicle mass than existing estimation methods employed in the same multi-parameter estimation context. The estimator allows prediction of vehicle mass whose limiting factor is the bandwidth and accuracy of the available driveline torque information, not the algorithm itself, allowing the identification of a 150kg change in mass on a 2000kg vehicle in this case study.