State of charge (SOC) estimation is of great significance for the safe operation of lithium-ion battery (LIB) packs. Improving the accuracy of SOC estimation results and reducing the algorithm complexity are important for the state estimation. In this paper, a zeroaxial straight line, whose slope changes along with SOC, is used to map the predictive SOC to the predictive open circuit voltage (OCV), and thus only one parameter is used to linearize the SOC-OCV curve near the present working point. An equivalent circuit model is used to simulate the dynamic behavior of a LIB, updating the linearization parameter in the measurement equation according to the present value of the state variables, and then a standard Kalman filter is used to estimate the SOC based on the local linearization. This estimation method makes the output equation of the nonlinear battery model contain only one parameter related to its dynamic variables. This is beneficial to simplify the algorithm structure and to reduce the computation cost. The linearization method do not essentially lose the main information of the dynamic model, and its effectiveness is verified experimentally. Fully and a partially charged battery experiments indicate that the estimation error of SOC is better than 0.5%.

For battery equivalent circuit model parameter identification, the fractional-order modeling and the bionic algorithm are two excellent techniques. The former can describe the impedance characteristics of batteries accurately, while the latter has natural advantages in solving some nonlinear problems. However, the high computational cost limits their application. In this article, a parameter-identification method for a battery fractional-order model based on the coevolutionary particle swarm optimization (CPSO) is proposed. In this algorithm, a large number of optimization calculations are dispersed between the adjacent sampling times in the form of evolutionary steps by CPSO, so the algorithm can run in real time with the sampling process. In addition, the simplified fractional approximation further reduces the computational cost. By conducting tests under various algorithm conditions, we evaluate the main factors affecting the algorithm performance in detail. Our results show that compared with the integer-order model, the fractional-order model can track the optimal value more effectively in a wider optimization space, CPSO can track the time-varying battery parameters in real time by continuous evolution, and computational costs can be effectively reduced by using a fixed-order fractional-order model and appropriately compressing the length of the historical data required for fractional-order computation.

A new robo-animal navigation principle and method which is based on virtual punishment and its related techniques including electrodes used, wireless remote control device and stimulation parameters set are introduced in detail in this paper. For this new method, encoding electrical signals with certain parameters are adopted to stimulate the thalamic ventral posterolateral nucleus (VPL) and amygdale nucleus (AMY) of the rat to guide its behavior and activity. Rats can change directions and escape actively because of feelings of virtual harm depending on the sites of brain stimulation. Compared with previous method based on virtual rewards, much better guide results are obtained, meanwhile minor stimulation intensity is enough and training in advance is no more necessary. Our results validate the feasibility and reliability of this new method. It makes it possible to manufacture robo-animal quantitatively and conveniently. This study will provide new approaches and prospects for investigating roboanimals and will accelerate its development and application.

The bio-robot research field is growing. Robo-pigeons have been successfully programmed to turn left or right; however, a satisfactory method of commanding a robo-pigeon to walk forward is still lacking. This problem has become a roadblock to progress in bio-robot research and applications. In mammals, the midbrain periaqueductal gray region (PAG) plays a key role in mediating defensive reactions in response to fear and anxiety. The avian intercollicular nucleus (ICo) is thought to correspond to the PAG. In this study, we found that microstimulating the ICo could successfully induce a robo-pigeon to walk forward. Compared with stimulation of the previously used archistriatum, the response time was considerably shorter and the behavior accuracy significantly higher. This paper describes in detail the process of controlling a robo-pigeon such that it walks forward and backward along a prescribed straight line. From the results, we draw the conclusion that the ICo is suitable for prompting the “walk forward” order in robo-pigeons.