# Research on Brake Energy Recovery Strategy Based on Working Condition Identification
## 4 Construction of Regenerative Brake Strategy Based on Condition Identification
### Optimisation of Regenerative Braking Parameters Based on \(Ga\)  
The efficiency of regenerative braking energy recovery is mainly affected by the vehicle velocity, battery charge state, battery charging power, electric motor characteristics, and the ratio of brake force distribution between mechanical and electric motor; the braking torque requirement of the electric motor can be calculated using the following equation:  
\[T_{ReqMot}=K_{1}\times T_{ReqWheel}\times(1-\beta)\times\gamma\times V_{k}\times \frac{SOC_{k}}{\eta_{t}} \tag{22}\]  
where \(T_{ReqWheel}\) is the vehicle demand brake torque; \(T_{ReqMot}\) is the electric motor brake demand torque; \(V_{k}\) is the velocity correction factor; \(SOC_{k}\) is the \(SOC\) correction coefficient; \(\eta_{t}\) is the transmission efficiency; \(K_{1}\) is the electric motor brake demand torque gain coefficient; and \(\gamma\) is the mechanical and electric motor brake force distribution coefficient, i.e., the ratio of the electric motor brake torque to the total brake torque of the rear axle. When \(SOC>60\%\), the battery limits the maximum charge power to limit the power generated by the electric motor, thus extending the life of the battery. \[P_{lim}=K_{2}P_{b\_max} \tag{23}\]  
where \(P_{lim}\) is the generating power of the electric motor; \(P_{b\_max}\) is the maximum charging power of the battery; and \(K_{2}\) is the maximum charging power gain coefficient of the battery. The electric motor speed affects the efficiency of the electric motor's power generation, and the regenerative braking minimum electric motor speed keeps the electric motor in a more efficient working range. \[P_{k}=\frac{T\times n_{k}}{9550} \tag{24}\]  
where \(P_{k}\) is the power at minimum speed and \(n_{k}\) is the minimum speed of regenerative braking, in rpm. In this study, the velocity correction factor \(V_{k}\) was set within the range [0.8, 1.2]; the battery SOC correction factor \(SOC_{k}\) was set within the range [0.8, 1.2]; the electric motor braking demand torque gain coefficient \(K_{1}\) was set within the range [1, 4]; the maximum battery charging power amplification coefficient \(K_{2}\) was set within the range [0.5, 1]; the minimum speed of the electric motor for regenerative braking \(n_{k}\) was set within the range [400, 800]; and the mechanical and electric motor braking force distribution coefficients \(\gamma\) were optimised in the range [0, 1] to obtain the best regenerative braking energy recovery efficiency. Figure 12: Brake force distribution curves for front and rear axles. Four driving conditions were defined and obtained, as described in Section 3; Figure 13 shows some of the time-velocity curves synthesised for the four conditions, where (a), (b), (c), and (d) were synthesised using only kinematic segments of the highway, suburban, urban smooth, and urban congestion working conditions, respectively, and parameter optimisation was performed under each of the four conditions. The genetic algorithm (GA) is an adaptive global search method for finding optimal solutions through the simulation of the evolutionary process, which defines the search space of optimal solutions as an evolutionary process and solves them with a set of stochastic rules. GAs do not have the restriction of the hard rules found in traditional algorithms, giving them superior parallel solving abilities and global optimality search abilities, and they can also effectively solve multi-objective optimisation problems; therefore, a GA was chosen for offline parameter determination in this study. The steps of the GA used in this study for parameter optimisation were as follows:  
Step 1: The target parameters and their constraints were determined, and the six control parameters were formed into an individual component of the genetic algorithm. Step 2: The target working conditions and the target load state were determined.