Aimed at the limitation of traditional fuzzy control strategy in distributing power and improving the economy of a fuel cell hybrid electric vehicle (FCHEV), an energy management strategy combined with working conditions identification is proposed. Feature parameters extraction and sample divisions were carried out for typical working conditions, and working conditions were identified by the least square support vector machine (LSSVM) optimized by grid search and cross validation (CV). The corresponding fuzzy control strategies were formulated under different typical working conditions, in addition, the fuzzy control strategy was optimized with total equivalent energy consumption as the goal by particle swarm optimization (PSO). The adaptive switching of fuzzy control strategies under different working conditions were realized through the identification of driving conditions. Results showed that the fuzzy control strategy with the function of driving conditions identification had a more efficient power distribution and better economy.
State of charge (SOC) plays a significant role in the battery management system (BMS), since it can contribute to the establishment of energy management for electric vehicles. Unfortunately, SOC cannot be measured directly. Various single Kalman filters, however, are capable of estimating SOC. Under different working conditions, the SOC estimation error will increase because the battery parameters cannot be estimated in real time. In order to obtain a more accurate and applicable SOC estimation than that of a single Kalman filter under different driving conditions and temperatures, a second-order resistor capacitor (RC) equivalent circuit model (ECM) of a battery was established in this paper. Thereafter, a dual filter, i.e., an unscented Kalman filter–extended Kalman filter (UKF–EKF) was developed. With the EKF updating battery parameters and the UKF estimating the SOC, UKF–EKF has the ability to identify parameters and predict the SOC of the battery simultaneously. The dual filter was verified under two different driving conditions and three different temperatures, and the results showed that the dual filter has an improvement on SOC estimation.
The estimation accuracy of single extended Kalman filter is not high, also it is affected by the initial value of state of charge (SOC). The second-order RC equivalent circuit model of lithium battery is established, and a joint algorithm, dual extended Kalman filter (DEKF) is proposed. Besides, the covariance matching theory is introduced for DEKF under the complex condition of uncertain noise statistical characteristics to improve the estimation accuracy. The improved DEKF is compared with another joint algorithms, i.e. recursive least squares and extended Kalman filter (RLS-EKF). Through the validation of battery test data, the modified dual extended Kalman filter based on covariance adaptive algorithm can realize real-time online estimation of battery SOC and time-varying parameters, and the estimation accuracy of lithium battery SOC and battery time-varying parameters is higher.
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